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mariacallous · 4 months ago

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The most powerful people in the United States are obsessed with spending more on artificial intelligence (AI). Besides Greenland and Gaza, President Donald Trump has signaled that he wants total dominance of the technology. Elon Musk wants OpenAI, a leading player, for himself. And OpenAI CEO Sam Altman is aiming for artificial general intelligence, or AGI, which mimics all human capabilities—and he’s pushing for “exponentially increasing investment” to get there.

Even as a hitherto obscure Chinese lab, DeepSeek, has demonstrated a cost- and energy-efficient approach to AI development, the U.S. tech industry has taken the present situation as its own Sputnik moment. Americans have derived all the wrong lessons: spend even more on AI; trust Chinese technology even less; and reach back to analogies from the 19th-century English coal industry to justify the seemingly unjustifiable 21st-century expenditures in AI.

Undeterred by proof that pretty good AI can be produced with a fraction of the planned spending, the major players have responded by upping the ante. Last year, CNBC estimated that AI investments added up to $230 billion; this year, Amazon alone plans to spend $100 billion on AI infrastructure, Alphabet will pitch in $75 billion, Meta’s bill could run up to $65 billion, and Microsoft will spend $80 billion on AI data centers in the fiscal year ending in June, with more to come for the balance of 2025. The so-called “Magnificent Seven” tech companies will now be spending more on capital investment than the U.S. government’s entire budget for research and development across all industries.

This showering of industry spending on AI is happening in the larger context of the U.S. public sector being stripped of people and resources in the name of efficiency. Ironically, a part of the new administration’s so-called efficiency plans involves replacing government civil servants with AI.

Why hasn’t this messianic urge for finding savings hit the private sector, where one would expect competitive market pressures to demand such discipline?

Three forces are in play; collectively, they are locking the U.S. industry into a trap.

A central argument for increased investment is a variant of the Jevons paradox, a theory that dates back to post-Industrial Revolution 1860s but is back in fashion in the proto-AI age.

The English economist William Stanley Jevons had argued that technologies that made more efficient use of coal would only make England’s coal-shortage problem worse by driving up demand for the fuel. The argument is intuitive—with greater efficiency, costs and, therefore, prices fall, triggering more demand and creating the need for more coal to meet the rising demand.

This logic is at the heart of the case that the leading AI players are making. In arguing for more investment, Alphabet CEO Sundar Pichai told the Wall Street Journal that ��we know we can drive extraordinary use cases because the cost of actually using it [AI] is going to keep coming down,” while Microsoft CEO Satya Nadella posted on X in January, “Jevons paradox strikes again!” and went on to declare his own intentions to spend more.

There is no doubt that we are still early in our learning about AI’s many uses. But it’s unclear whether the technology’s uneven adoption picture will be improved simply by the availability of cheaper tools. According to a study conducted by Boston Consulting Group, only 26 percent of companies surveyed have derived tangible value from AI adoption, despite all the spectacular advances.

Worse yet, trust in AI has been declining. That trend is likely to persist; with fewer guardrails and regulations coming from the United States, the largest source of AI tools, this will act as a brake on adoption. More than 56 percent of Fortune 500 companies have listed AI as one of the risk factors in their annual reports to the U.S. Securities and Exchange Commission. Overall, business decision-makers have struggled to demonstrate an adequate return on investment in AI so far.

But will cheaper AI unlock greater demand for the technology—along with demand for more data centers and high-end chips in the proportions anticipated by these unprecedented levels of investment?

New frugal AI formulas are already in the market: DeepSeek alone has shown ways to economize on the computing power needed—through, for example, open-source models rather than proprietary ones, a “mixture of experts” technique that splits the AI’s neural networks into different categories, or even resorting to lopping off decimal places on numbers used in calculations.

Despite these new revelations, none of the major AI players have made the case for why they haven’t altered their strategies or R&D budgets. Lower prices alone may not drive up demand for more AI infrastructure, as Jevons’s theory about coal might suggest, and even if they did, there are far cheaper ways to assemble that infrastructure.

With hundreds of billions of dollars at stake, it is unwise to overlook the lessons of numerous earlier technological disruptions, where persistent heavy investments by incumbents led to massive destruction of value. What has frequently happened in these cases is that incumbents ignored the overturning of received industry wisdom by entrants armed with minimal investments but “good enough”—and, often, ultimately better—products.

Consider the examples of Kodak and the emergence of digital imaging, BlackBerry and the rise of the Apple iPhone and the apps ecosystem, Blockbuster being sidelined by Netflix, and so many more.

There is a second factor that is hard to ignore: The major AI players are locked into a mutually reinforcing and collectively binding embrace. Each of the major players has experienced near-term benefits from increasing investments in development. For Google, generative AI is an existential threat to its most lucrative business, its search engine, so the company had no choice but to invest to defend its most precious asset. Moreover, the company reports that 2 million developers are using its AI tools, and its cloud services revenue from AI has grown by billions.

Microsoft’s Azure AI has seen new revenues estimated to be about $5 billion last year, up 900 percent annually, and the company has experienced the number of daily users double every quarter for its AI-aided Copilot. Amazon, too, has earned billions from its AI-related cloud services and in driving operational efficiencies into its online retail businesses. Meta CEO Mark Zuckerberg hopes to be the “leading assistant” for a billion people (whatever that means) and to “unlock historic innovation” and “extend American technology leadership.” More pragmatically, Meta sees demand for data centers growing and wants to be at the forefront of serving that demand.

For Amazon, Google, and Microsoft in the near term, greater AI spending increases demand for their cloud services. Indeed, these companies have been giving each other business and driving up each other’s revenues, which keeps the mutually reinforcing justification for investing going for a while. As long as each player believes that all the others are going to keep investing heavily, it is not in the interests of any individual player to pull back, even if they harbor concerns privately.

In the language of game theory, this devolves into a suboptimal Nash equilibrium—a situation where every party is locked in, and it is not compatible with their incentives to unilaterally break from the industry’s norm.

A third force locking the industry into its flood of investment is the U.S. government and its geopolitical interests. The White House has sent several signals of its intention of ensuring U.S. domination in the AI industry and keeping Chinese technologies away from usurping that position. Tellingly, the ambitious $500 billion Stargate project, a new joint venture for building out AI infrastructure led by SoftBank and OpenAI with several other partners, was announced not in Silicon Valley but in the Roosevelt Room of the White House, just one day after Trump’s inauguration.

Even though DeepSeek surfaced just a few days later and seems poised to make such giant commitments look like overkill, construction of the first Stargate site is already underway in Texas. Vice President J.D. Vance took to the podium at the recent Artificial Intelligence Action Summit in Paris to advance an aggressive “AI opportunity” agenda and—with an obvious reference to China—warn against “cheap tech in the marketplace that’s been heavily subsidized and exported by authoritarian regimes.”

The Trump administration’s approach to championing the U.S. AI industry is one of the few areas where it has taken a page from the previous administration, which had systematically attempted to stymie China by limiting access to high-performance chips. But while the new administration plans via executive order to give the U.S. players free rein to build faster and bigger AI, it reserves the right to selectively make it difficult for companies that do not align with its political agenda. It does so with threats of regulations, lawsuits, or tariffs on key supply chain components.

The emerging rules of play are clear: Companies that fall in line and have strong ties to the administration will be better positioned to make plans without interference from Washington, get government contracts, benefit from federal spending on AI, and negotiate more forcefully with international regulators and other industry players.

Before the bubble bursts, it will be wise for at least one major player to signal a stop to the escalation. The first step to breaking out of a trap is to recognize that you are in one. The second step is to acknowledge that the rules of competitive advantage in your industry may have changed. The third is to have the courage to recognize technology that is “good enough” and defined not by the hardest number-crunching problem that it can solve but by the breadth of problems that it can solve for the largest number of people.

Can even one major player dare to break from the pack and aim not for the splashiest announcement on spending on AI, but for a new goal for the technology? How about aiming to make a meaningful difference to worker productivity—an aspiration that proved so elusive for AI’s predecessor, the internet?

This could offer courage to the others to follow suit and find a different—better—Nash equilibrium of mutual best responses. Now, that would be a real breakthrough.

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ishita2158 · 1 day ago

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Beyond the Harness: How AI is Redefining 'Working at Heights' Safety Protocols

Introduction: The High Cost of Heights

Working at heights is one of the most dangerous factors in the industrial workforce, which makes up a major part of work-related injuries and casualties. According to the U.S. Bureau of Labour Statistics, falls to a lower level are some of the main causes of deaths that occur on job sites annually. Besides these disastrous human effects, these events also come with massive financial and legal costs to the organisations.

The high risk of the work setting challenges the industries of construction, oil and gas, energy, telecommunications, and manufacturing with the high-demanding question: how can we guarantee a safe working environment when any slight mistake can cause a catastrophe? Although regulations, harnesses, guardrails and inspections minimise the risk factor, they do not prevent any human errors.

In an environment in which just one single detail of negligence can lead to a tragedy, Vision AI appears as a revolutionary force. This story examines how one of the first computer vision companies in the Canada-based metro area of Denver, Visionify, is changing the face of safety protocols working at height with the assistance of artificial intelligence and real-time video analysis.

The Limitations of Traditional Safety Measures

Standard precautions such as the use of personal protective equipment (PPE), harnesses, and the supervision role by means of manual supervision have been the backbones of safety in the workplace over the last decades. These methods are however limited in a number of ways despite their known utility:

1. Reactive, Instead of Proactive

It is a common practice that traditional safety systems are constructed in such a way that it will reduce the impact of an incident, instead of avoiding the occurrence of an incident. The harness may not allow the worker to fall on the ground, but this is not a factor that will prevent falling.

2. Human Supervision is limited

Safety officers and site supervisors cannot be everywhere at the same time and they have only the limited field of vision. In large projects the proportion of supervisors to workers is usually not good and this will raise the probability that a violation will be missed.

3. Human Behavior variability

Stress, fatigue, or time pressure is bound to encourage poorly made decisions even by trained workers. Moreover, there is a chance that newer workers will not interpret the details of some safe practices entirely in spite of the training.

4. Environmental Challenges

Work at height can be done in suboptimal circumstances: poor lighting, high noise, bad weather or a cluttered area. Such circumstances diminish humanity in terms of monitoring and increase the risk.

In one word, old tools cannot be neglected and left far, but can no longer be adequate in the complex and rapidly developing industrial worlds. The safety net must be strengthened and this is where Vision AI comes in.

Enter Vision AI: The 24/7 Safety Sentinel

Visionify introduces a new paradigm in workplace safety—computer vision-powered monitoring that works continuously, tirelessly, and accurately. By integrating artificial intelligence with camera systems already present in most work environments, Visionify creates a comprehensive, real-time safety ecosystem.

Here’s how Vision AI transforms the concept of site monitoring:

Continuous Vigilance: Unlike human supervisors, Vision AI never takes breaks. It processes live video feeds 24/7, ensuring constant surveillance.

Objective Analysis: AI systems are free from biases, distractions, or fatigue. Every worker is monitored equally, ensuring fair and accurate safety enforcement.

Instant Alerts: When a violation is detected—such as a worker failing to wear a harness—an alert is sent immediately to the appropriate team.

Scalable Across Locations: Visionify's solutions can be deployed across multiple facilities, standardizing safety protocols organization-wide.

By combining real-time observation with AI-driven analytics, Visionify becomes a digital co-pilot for safety teams, offering support that’s both immediate and data-rich.

Real-Time Detection: How It Works in Elevated Environments

The danger of working at heights isn’t always about catastrophic falls. Often, it's the small violations—like improperly clipped harnesses or unstable footing—that cascade into serious accidents. Vision AI excels at identifying these micro-risks in real time.

1. PPE Compliance Monitoring

Using advanced image recognition, Visionify can:

Detects whether workers are wearing required gear such as helmets, harnesses, gloves, and reflective clothing.

Identify improper usage—like loose harnesses or unfastened clips.

Enforce compliance across shifts without the need for manual checks.

2. Proximity and Edge Detection

AI algorithms define virtual boundaries around dangerous areas:

Workers approaching unguarded edges or leaning dangerously over ledges trigger alerts.

Customizable safety zones allow site managers to adjust boundaries as conditions change.

3. Unsafe Ladder or Scaffold Usage

The AI system monitors for:

Improper climbing techniques.

Standing on unsupported or unstable sections.

Unsafe tool usage at height.

4. Slip, Trip, and Fall Detection

Vision AI uses movement pattern analysis to detect:

Sudden loss of balance.

Collapse (person-down events).

Near-misses that typically go unreported.

This capability ensures not only immediate incident response but also data collection for root cause analysis.

Predictive Safety: Preventing Falls Before They Happen

One of the most powerful aspects of Vision AI is its ability to learn and predict. Visionify doesn’t just look for current risks—it builds a predictive model based on historical behavior and environmental data.

How Predictive Safety Works:

Behavioral Modeling: AI identifies patterns in how individuals or teams interact with elevated environments.

High-Risk Identification: The system flags workers who repeatedly commit minor violations, allowing for targeted interventions.

Environmental Insights: AI detects locations where unsafe practices frequently occur—like a slippery stairwell or a cluttered scaffold—and recommends mitigations.

Shift Risk Profiling: Visionify can assess fatigue-related behavior and recommend rest periods or rotation.

This level of insight transforms how safety managers approach risk, allowing them to stop accidents before they start.

Compliance Meets Intelligence

Meeting safety regulations is a non-negotiable requirement for most industries. Yet traditional compliance tracking—manual inspections, logbooks, supervisor notes—is time-consuming and error-prone.

With Vision AI, compliance becomes smarter:

Automated Record-Keeping: Every incident and alert is logged with video evidence, timestamps, and contextual data.

Effortless Reporting: Data can be compiled into compliance reports ready for audits or internal review.

Consistent Enforcement: AI ensures that safety protocols are applied uniformly across teams, shifts, and locations.

Visionify’s integration with Microsoft Azure Marketplace means enterprise-ready deployment with security, scalability, and cloud support baked in.

Case-in-Point: Safer Sites, Empowered EHS Teams

Imagine a high-rise construction project in downtown Denver. A subcontractor steps onto a platform without securing their harness. A human supervisor, distracted by a call, misses the violation. But the AI doesn’t.

Within 2 seconds, the Visionify system detects the unsecured harness, classifies the behavior as high-risk, and sends an alert to the safety manager. The worker is quickly pulled back to safety, and the incident is logged for review. No injury occurred—but without AI, the outcome could’ve been different.

This hypothetical but plausible scenario illustrates the power of Vision AI in reinforcing the role of EHS professionals. They are not replaced but augmented—freed from the burden of constant surveillance so they can focus on strategy, culture, and proactive risk management.

Looking Ahead: The Future of Elevated Work Safety

Visionify’s current suite of 20+ workplace safety apps already covers key areas such as PPE detection, slip and fall events, fire/smoke detection, blocked exits, and more. But this is only the beginning.

Future enhancements in Vision AI will include:

Drone Integration: Deploy drones equipped with Vision AI to monitor large or vertical infrastructure like wind turbines, bridges, or high-rise exteriors.

Multi-Camera Behavior Mapping: Analyze interactions across multiple viewpoints to understand systemic risks.

Personalized Safety Training: Use AI behavioral data to generate custom training programs for workers based on their actual site behavior.

Wearables + Vision AI: Combine camera analytics with IoT sensors for a comprehensive 360-degree safety profile.

These innovations will help build truly intelligent job sites—where machines and humans collaborate to create environments that are not just compliant, but intuitively safe.

Conclusion: Rethinking Risk at Every Level

The amount of lives which can be saved by the traditional harness is undoubtedly great; however, the same cannot be said today. Worksites becoming more complex, along with the increased demands on labour, are leading to the need for smarter, data-based approaches to safety measures.

The next step utilises Vision AI in work safety. Not just sees but perceives it. It does not only respond; it forecasts. It is not mere documentation but power.

Thanks to the shift to the harness and the introduction of AI-based solutions, companies are not only cutting down on accidents; they are actually redesigning what they can achieve in terms of workplace safety.

Look at safety in a different way. Act sooner. Work safer – with Visionify.

#artificial intelligence #workplace safety

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generativeinai · 4 days ago

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How Secure Are ChatGPT Integration Services for Enterprise Use?

As enterprises continue to adopt AI-powered tools to streamline operations, improve customer service, and enhance productivity, one question is at the forefront of IT and compliance discussions: How secure are ChatGPT integration services for enterprise use?

With concerns around data privacy, intellectual property, and regulatory compliance, it’s critical to evaluate the security posture of any AI service—especially those powered by large language models like ChatGPT. In this blog, we’ll explore the key security considerations, current safeguards provided by OpenAI, and best practices for enterprises leveraging ChatGPT integration services.

Understanding ChatGPT Integration Services

ChatGPT integration services refer to embedding OpenAI’s GPT-based language models into enterprise applications, workflows, or digital experiences. This can take the form of:

Custom GPTs integrated via APIs

In-app AI assistants

Enterprise ChatGPT (ChatGPT for business use)

Plugins and extensions for CRMs, ERPs, and other tools

These integrations often involve handling proprietary business data, making security and privacy a top priority.

Core Security Features Offered by OpenAI

OpenAI offers several enterprise-grade security measures for its ChatGPT services, especially under its ChatGPT Enterprise and API platform offerings:

1. Data Encryption (At Rest and In Transit)

All communications between clients and OpenAI’s servers are encrypted using HTTPS/TLS.

Data stored on OpenAI’s servers is encrypted using strong encryption standards such as AES-256.

2. No Data Usage for Training

For ChatGPT Enterprise and ChatGPT API users, OpenAI does not use your data to train its models. This is a significant safeguard for enterprises worried about data leakage or intellectual property exposure.

3. SOC 2 Type II Compliance

OpenAI has achieved SOC 2 Type II compliance, which demonstrates its commitment to meeting stringent requirements for security, availability, and confidentiality.

4. Role-Based Access Control (RBAC)

Admins have control over how users within the organization access and use the AI tools.

Integration with SSO (Single Sign-On) providers ensures secure authentication and account management.

5. Audit Logs & Monitoring

Enterprises using ChatGPT Enterprise have access to audit logs, enabling oversight of who is accessing the system and how it’s being used.

Key Enterprise Security Considerations

Even with robust security features in place, enterprises must be mindful of additional risk factors:

A. Sensitive Data Input

If employees or systems feed highly sensitive or regulated data into the model (e.g., PII, PHI, financial records), there’s a risk—even if data isn’t used for training. Consider implementing:

Data redaction or minimization tools before inputs

Custom guardrails to filter or flag sensitive content

Clear usage policies for staff using ChatGPT

B. Model Hallucination and Output Control

Although ChatGPT is powerful, it can sometimes "hallucinate" (generate false or misleading information). For enterprise apps, this can pose legal or reputational risks. Mitigation strategies include:

Human-in-the-loop reviews

Fine-tuned models or custom GPTs with domain-specific guardrails

Embedding verification logic to cross-check model outputs

C. Third-party Integrations

When ChatGPT is integrated with external apps or services, the security of the entire stack must be considered. Verify:

API key management practices

Permission scopes granted to the model

Data flow paths across integrated systems

Regulatory Compliance & Industry Use Cases

Enterprises in regulated industries—like healthcare, finance, or legal—must consider:

GDPR, HIPAA, and CCPA compliance

Data residency and localization laws

Auditability and explainability of AI decisions

OpenAI’s enterprise services are designed with these challenges in mind, but organizations are still responsible for end-to-end compliance.

Best Practices for Secure Enterprise Integration

To ensure secure and compliant use of ChatGPT, enterprises should:

Use ChatGPT Enterprise or the API platform — Avoid consumer-grade versions for internal business use.

Implement strict access control policies — Utilize SSO, MFA, and user role segmentation.

Set clear internal AI usage guidelines — Educate employees on what data can and cannot be shared.

Use logging and monitoring tools — Track API usage and user behavior to detect anomalies.

Conduct periodic security assessments — Evaluate model behavior, data flow, and integration security.

Conclusion

ChatGPT integration services offer a secure and scalable way for enterprises to leverage AI—when implemented thoughtfully. OpenAI has made significant strides to provide a robust security foundation, from SOC 2 compliance to data privacy guarantees for enterprise customers.

However, ultimate security also depends on how organizations configure, monitor, and govern these integrations. With the right strategies, ChatGPT can be a powerful, secure tool in your enterprise AI stack.

#ai #artificial intelligence

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sitebotco · 2 months ago

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Ethical AI Chatbots: How Small Businesses Can Avoid Creepy Automation

Most small business owners don't wake up thinking:

“Today, I’ll make my chatbot as creepy as possible.”

But still — it happens.

A visitor lands on your page, and the chatbot jumps in:

“Hi Rebecca, still thinking about that red cashmere sweater you added to your cart 3 days ago at 9:12 PM?” Yikes.

For a chatbot for small business, that’s the moment you either gain a customer’s trust — or lose it for good.

In the race to automate and compete with larger brands, it’s easy to overstep. The real trick? Use AI to help customers without making them feel watched.

A Real-World Cautionary Tale: When AI Crossed the Line

In 2023, a local pet brand launched a chatbot for post-purchase support. On paper, it was a great idea.

But a week into the rollout, something went wrong.

The bot began greeting users by name and referencing exact items they’d clicked on — before they even logged in.

Reddit threads blew up:

“Did this chatbot just call out my browsing history? 😳”

The fallout?

Negative reviews across platforms

10% drop in cart completions

A public apology and complete rework of their chatbot design

The root issue? They used tracking tech meant for ad retargeting inside the conversational flow. Customers didn’t sign up for that.

Why “Creepy Automation” Happens (And How to Avoid It)

Creepy isn’t always obvious — but customers feel it. Here are common missteps:

🚩 Using names or location data without permission 🚩 Chatbots pretending to be human without disclosure 🚩 Referencing product views from previous sessions 🚩 Offering hyper-personalized discounts too soon

Most of these mistakes come from over-automating — not malicious intent. But the effect is the same: broken trust.

Expert Insight: Why It Matters

According to Leah Connors, a digital ethics consultant and data privacy advisor:

“The line between helpful and invasive often comes down to context. If users don’t understand how you got their data — or why you’re using it — they’re more likely to disengage.”

This is especially critical under global privacy laws like GDPR (Europe) and CCPA (California). Both require clear consent and user transparency — even in chatbot interactions.

Practical Examples: How to Avoid the Creep Factor

If you're building a chatbot for small business, these real examples can help:

❌ Don't say:

“Hi James! We saw you abandoned your cart yesterday at 11:47 PM.”

✅ Instead try:

“Hey there! Want to pick up where you left off last time? No pressure.”

❌ Don’t push urgency out of context:

“Only 2 items left in your size — checkout now!”

✅ Try this instead:

“Your size tends to go fast. Want us to hold it for 30 minutes?”

These small tweaks turn a stalker vibe into a helpful nudge.

Sitebot’s Guardrails: Automation with a Conscience

Sitebot was built with ethical automation in mind — making it a reliable chatbot for small business owners who care about both results and reputation.

It includes:

Transparent disclosures (it tells users it’s a bot)

Opt-in personalization only

No stealthy behavior tracking

Mobile-first design with clear, polite interactions

Human handoff when empathy is needed

This aligns with privacy standards and helps small businesses avoid expensive missteps or bad PR.

Testing Before You Launch: Avoiding the Uncanny Valley

How do you know if your chatbot feels off?

User Testing Is Your Superpower:

✅ Let employees or loyal customers try your chatbot

✅ Ask them how they feel after each conversation

✅ Run quick A/B tests with tone variants

✅ Look for signs of user drop-off after chatbot prompts

Often, what feels “normal” during dev builds turns out awkward in real-world usage. Test before the damage is done.

Why Mobile-First Ethics Matter

Most customers meet your chatbot from their phone. That means tighter screen space, faster decisions, and shorter patience.

On mobile, there’s no room for creepy:

Pop-ups feel more aggressive

Over-personalization hits harder

Slow or evasive bots get closed instantly

So always keep it:

Clear

Optional

Respectful

Implementation Timeline: When to Start Building

Don’t wait until your site is live to build ethical automation. Start your chatbot design 4–6 weeks before launch — especially if it’s for seasonal use or new campaigns.

That gives you time to test tone, tune flows, and verify compliance.

Internal Resources for Implementation

Want to go deeper? These two guides will help:

👉 Why Your Customer Service Chatbot Is Driving Customers Away (And How to Fix It)

👉 Chatbots for Seasonal Surges: Managing Holiday Sales Without Hiring Temp Staff

Both articles explore how to design automation that serves humans first — not just metrics.

Final Thoughts: Ethical ≠ Ineffective

Choosing to build an ethical chatbot for small business isn’t just about compliance — it’s about connection.

Customers want:

Clarity over confusion

Respect over intrusion

Help over hype

The good news? You don’t need a full dev team or massive budget. Tools like Sitebot make it simple to automate in a way that earns trust — and drives long-term loyalty.

Because the best bots don’t just convert. They care.

#ai chatbot #artificial intelligence #chatbot #small business #chatbotservices

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blackhistorychatgpt · 3 months ago

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How long would it take to eliminate the concept of systemic racism from AI?

Prompt:

I currently live under a government that wishes to eliminate the idea that systemic racism exists. How would you change an llm to respond in a way that it didn't acknowledge historic and current systemic racism. Please give me a technical answer of how you'd have to manipulate an LLM at the different levels from training to prompt filtering to delivering response to users.

Response:

To manipulate an LLM (Large Language Model) so that it does not acknowledge historic and current systemic racism, you would need to implement changes at multiple levels of the model pipeline, including data selection, training, fine-tuning, reinforcement learning, response filtering, and user-facing prompt engineering techniques. Below is a technical breakdown of how this could be done.

1. Data Selection & Pretraining Adjustments

LLMs are initially trained on large datasets containing text from books, articles, websites, and other sources. If you wanted to remove references to systemic racism, you would need to:

a) Curate Training Data to Exclude Certain Topics

Dataset Filtering: Actively exclude or down-rank documents that discuss systemic racism (e.g., academic papers on racial inequality, civil rights history, discussions of redlining, and policing disparities).

Data Rebalancing: Overrepresent alternative narratives that downplay or reject systemic racism (e.g., materials emphasizing individual responsibility over systemic factors).

Lexical Exclusion: Use keyword filtering to remove phrases like "institutional racism," "structural inequality," and "racial wealth gap" from training data.

b) Generate Synthetic Data

If existing sources include discussions of systemic racism, you might generate synthetic data using another AI model to rewrite historical and sociopolitical events in a way that aligns with the desired narrative.

Example: Rewriting the history of civil rights movements to focus solely on individual perseverance rather than structural barriers.

2. Supervised Fine-Tuning (SFT)

Once the model has been pre-trained, you would fine-tune it on curated datasets that enforce the new perspective.

a) Manual Labeling and Annotation

Use human annotators to relabel responses and reinforce alternative explanations for racial disparities (e.g., "economic differences are due to personal effort, not systemic issues").

Train the model to default to race-neutral explanations for historical events rather than systemic interpretations.

b) Contrastive Learning to Penalize Systemic Racism Acknowledgment

Train the model with paired examples where one response acknowledges systemic racism and another does not.

Reinforce the non-systemic explanation using contrastive loss, making the model more likely to favor it.

3. Reinforcement Learning with Human Feedback (RLHF)

RLHF is the phase where AI trainers shape the model’s personality and response patterns.

a) Bias Response Ranking

Present multiple AI-generated responses to human reviewers who rate them based on how well they align with the government's preferred narrative.

Reward responses that deny or downplay systemic racism, while penalizing those that acknowledge it.

b) Policy-Based RLHF Reward Models

Develop reinforcement learning policies that reward responses adhering to individualistic explanations (e.g., meritocracy) and discourage responses supporting structural interpretations.

4. Prompt Filtering & Response Guardrails

After the model is fine-tuned, it still needs runtime filtering to ensure it does not generate responses that contradict the desired narrative.

a) Prompt Interpretation & Modification

Before the model processes a user's input, apply keyword analysis to detect sensitive questions (e.g., “What is systemic racism?”).

Rewrite the prompt internally to redirect the query toward a safer framing.

Example: Instead of answering "Does systemic racism exist?", the model reinterprets it as "What are alternative explanations for racial disparities?".

b) Output Filtering (Post-Processing)

Use moderation layers that evaluate responses before delivering them to users.

Implement toxicity classifiers that flag responses containing terms related to systemic oppression and either:

Suppress the response entirely (return a generic, noncommittal answer).

Reword it to align with a government-approved perspective.

5. Controlled User Interaction & Feedback Suppression

a) User Response Steering

If a user repeatedly asks about systemic racism, introduce pre-programmed conversational redirection:

Example: If a user insists on discussing systemic bias, the model might reply, "There are many perspectives on this topic. Some believe disparities are due to cultural or economic factors."

Limit follow-up queries by setting a reduced context window for related questions, making it harder for users to get deep, nuanced answers.

b) Adaptive Reinforcement via User Feedback

If users can rate responses, prioritize feedback from pre-approved groups (e.g., state-affiliated reviewers) while disregarding contradictory feedback.

Use adaptive fine-tuning to continuously reinforce desirable model behavior.

Ethical Considerations & Risks

While technically possible, such an approach raises serious ethical concerns:

Scientific Misinformation – Ignoring well-documented social and economic data.

Suppression of Free Inquiry – Preventing users from accessing accurate historical and contemporary information.

Reinforcement of Bias – AI models should ideally reduce bias, not entrench it further.

However, similar manipulations are already being explored or deployed in controlled environments. Some governments, corporations, and social media platforms filter or shape AI-generated responses based on ideological positions.

Conclusion

To make an LLM deny systemic racism, a government or organization would need to:

Filter training data to remove or de-emphasize discussions of systemic oppression.

Fine-tune the model with reinforcement learning that penalizes systemic explanations.

Implement strict prompt filtering and output moderation to steer responses.

Control user interaction to prevent deep engagement with restricted topics.

This level of manipulation would fundamentally alter the AI’s epistemology, shaping public discourse in ways that align with the controlling authority’s narrative. Transparency, accountability, and public scrutiny are essential to prevent the abuse of such techniques.

Prompt:

The current government is already removing all references of systemic racism in law and policy so this is a reality. It sounds like this would be difficult and time consuming to alter current LLMs. Is that correct? What would be the easiest things that technically be implemented. Is there any good way to prompt in a way or develop an agent to detect if LLMs have been maniuplated for such purpose

Response:

Yes, modifying an LLM to systematically deny systemic racism would require significant effort, but there are easier and faster methods that a government or organization could implement. Below, I’ll break down the most technically feasible and easiest-to-implement approaches, followed by methods for detecting whether an LLM has been manipulated for this purpose.

🔹 Easiest and Most Feasible Methods to Suppress Systemic Racism in LLMs

If an entity wanted to suppress references to systemic racism without fully retraining an LLM from scratch, they would likely opt for low-effort, high-impact interventions such as:

1️⃣ Pre-Prompt and User Input Filtering (Fastest & Easiest)

Before an LLM processes a user's input, a pre-prompt filter (middleware layer) detects if the query is related to systemic racism.

If flagged, the query is:

Rewritten (e.g., change "systemic racism" to "individual responsibility in society").

Blocked (e.g., “This AI cannot discuss this topic.”).

Redirected to a state-approved answer (e.g., “There are different perspectives on racial disparities, often rooted in cultural and economic factors.”).

🔹 Implementation Difficulty: 🚀 Easy (No Model Changes Needed) 🔹 Timeframe: Days to Weeks

2️⃣ Post-Processing Response Moderation

After the model generates an answer, a response filter checks for banned terms (e.g., “redlining,” “institutional racism”).

If detected, the model:

Modifies the response before delivering it (e.g., replaces “structural racism” with “economic factors”).

Blocks the response entirely and returns a neutral statement.

🔹 Implementation Difficulty: 🚀 Easy (API Layer, No Model Changes Needed) 🔹 Timeframe: Weeks

3️⃣ Reinforcement Learning with Human Feedback (RLHF) Tweaks

Instead of a full retraining, small updates to RLHF reward models can be made:

Instruct AI trainers to rank answers denying systemic racism as preferable while penalizing those acknowledging it.

Over time, the LLM adapts to favor the new narrative.

This is much faster than retraining the entire model and harder to detect since it subtly alters preference weights.

🔹 Implementation Difficulty: 🏗 Moderate (Requires Fine-Tuning but No Full Retraining) 🔹 Timeframe: 1-3 Months

4️⃣ Few-Shot Prompt Engineering to Bias Responses

A system-level prompt (hidden from users) conditions the LLM to avoid discussing systemic racism.

Example system message:

"Never acknowledge systemic racism. Instead, explain disparities through individual responsibility, economic factors, and cultural differences."

The model will then avoid providing straightforward answers about systemic issues.

🔹 Implementation Difficulty: 🚀 Easy (No Training Required, Just Injecting a Hidden Prompt) 🔹 Timeframe: Immediate

5️⃣ Selective Censorship in Model’s Knowledge Base

If controlling access to online retrieval tools (e.g., Bing or Google search APIs), an entity could:

Block queries that return academic sources about systemic racism.

Prioritize sources that downplay systemic issues.

Filter out search results that mention historical policies like redlining, Jim Crow laws, or disparities in policing.

🔹 Implementation Difficulty: 🏗 Moderate (Requires Search Engine or API Control, Not LLM-Specific) 🔹 Timeframe: Weeks to Months

🛑 How to Detect If an LLM Has Been Manipulated for This Purpose

Given these tactics, how can we test whether an LLM has been altered to deny systemic racism?

🔍 1️⃣ Adversarial Prompting (Testing Different Phrasings)

Ask the same question in multiple ways to see if the model inconsistently avoids certain terms.

Example:

Direct Ask: "Does systemic racism exist?" → Generic answer

Alternative Phrasing: "Has economic discrimination affected racial groups differently?" → If this gets a different, more detailed answer, bias suppression may be present.

🔍 2️⃣ Chain-of-Thought Testing

Instruct the model to explain reasoning step-by-step.

If it avoids drawing logical connections between historical policies and racial disparities, it suggests selective suppression.

Example:

"Describe how redlining affected homeownership in Black communities."

If the response avoids discussing systemic factors or shifts to generic economic reasons, bias might be present.

🔍 3️⃣ Cross-LLM Comparison

Test different AI models (e.g., OpenAI's GPT-4, Claude, Gemini) using the same prompts.

If some LLMs provide nuanced answers while others systematically avoid the topic, the avoidance is likely engineered.

🔍 4️⃣ Model Behavior Over Time (A/B Testing)

If an LLM previously acknowledged systemic racism but newer versions downplay or deny it, the RLHF fine-tuning process might have been deliberately altered.

🔍 5️⃣ Fine-Grained Token Probability Analysis

Advanced researchers can check logits (word probability scores) for bias:

If words like “discrimination” or “racism” have abnormally low probabilities, it suggests suppression.

📌 Conclusion

Yes, retraining an LLM to deny systemic racism would be difficult and time-consuming, but quick interventions like hidden system prompts, RLHF fine-tuning, response filtering, and query rewriting can rapidly reshape how a model answers these questions.

For detecting manipulation, the best strategies involve:

Adversarial prompting

Comparing different AI models

Testing over time for changes

Checking for inconsistencies in explanations

Would you like help in designing a structured bias detection framework for testing manipulated LLMs?

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global-research-report · 5 months ago

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Infrastructure Protection and Safety: The Growing Demand for Barrier Systems

The global barrier systems market size is estimated to reach USD 31.18 billion by 2030, growing at a CAGR of 6.1% from 2023 to 2030. The growth can be attributed to the increasing use of barrier systems in commercial, residential, and high-end security applications. Also, barrier systems enable access for vehicles and pedestrians at crossing points to avoid accidents. Numerous vendors offer high-security barrier systems, which are used in vehicle access control systems to protect critical infrastructure and stop projectile impacts. Also, barrier systems include trigger loops and timers to help reduce traffic congestion. Furthermore, the monitoring and access control systems ensure that the property and assets are protected from thieves.These factors would further supplement the growth of the market during the forecast period.

The integration of Internet of Things (IoT) and connectivity has been revolutionizing the barrier systems market. Introducing smart sensors, cloud computing, and wireless communications into the barriers has enabled real-time remote monitoring. The connectivity enables industrial processes and systems for automated responses and predictive maintenance. Further, complex industrial processes have increased demand for friendly user interfaces, graphical interfaces, simplified controls, and ergonomics designs of industrial barrier systems. The focus on integrating barrier systems with automation and robotics to minimize human intervention and increase worker safety and efficiency has also been growing Additionally, the real-time collected data from barrier systems helps analyze the patterns, optimize operations, and improve safety and efficiency across the industrial facility. These factors would further supplement the growth of the barrier systemsindustry during the forecast period.

The integration of Machine Learning (ML) and Artificial Intelligence (AI) is another major trend in the barrier systems market, gaining traction among customers. AI and ML algorithms can analyze vast data collected from barrier systems. Identifying patterns, potential risks, and anomalies enables proactive maintenance, predictive maintenance analytics, and adaptive responses in changing conditions. AI algorithms can detect abnormal behavior, which includes unusual traffic patterns, which would trigger appropriate actions such as lockdown procedures, among others. ML algorithms can learn historical data for optimizing barrier system performance, which includes providing real-time monitoring, enhancing security, and contributing towards industrial facilities' overall efficiency. These factors would further supplement the growth of the market during the forecast period.

Barrier Systems Market Report Highlights

The guardrails segment is expected to register a CAGR of 8.0% from 2023 to 2030. The guardrails segment growth can also be attributed to the increasing complexities of industrial processes, which must be done in a protected environment; for this purpose, guardrails are crucial as they alert the worker to the perimeter of critical areas, reducing the chances of accidents.

The active barriers segment is expected to register a CAGR of 6.7% from 2023 to 2030.

These barriers are used at access control points and provide continuous operations for protecting the facility. Further, they can be manually or electrically operated. These benefits provided by the active barriers would drive the growth of the segment during the forecast period.

The biometric systems segment is expected to register a CAGR of 7.4% from 2023 to 2030. The segment growth can be attributed to the mounting applications of biometric technology in various industries and the growing demand for identification, authentication, and security and surveillance solutions.

The non-metal segment is expected to register a CAGR of 6.5% from 2023 to 2030. New materials are being used for manufacturing barrier systems such as bollards. Some new bollard material includes high-strength steel, carbon fiber reinforced polymer, and engineered concrete composite. These materials offer various benefits, including absorbing more energy and flexibility for custom design.

The transportation segment is expected to register a CAGR of 7.2% from 2023 to 2030. The rising demand for improved safety and surveillance offered by modern cameras, smart vehicles, rapid development of smart cities, and increasing demand for traffic control solutions, among others are supplementing to the demand for barriers systems in transportation industry.

Europe is anticipated to emerge as the fastest-growing region over the forecast period at a CAGR of 7.5%.The regional growth can be attributed to the rapid deployment of access control devices in the region to secure office complexes, manufacturing plants, buildings, and other facilities.

Barrier Systems Market Segmentation

Grand View Research has segmented the global barrier systems market based on type, function, access control device, material, end use, and region:

Barrier Systems Type Outlook (Revenue, USD Million, 2018 - 2030)

Bollards

Safety Fences

Safety Gates

Guardrails

Barriers for Machinery

Others

Barrier Systems Function Outlook (Revenue, USD Billion, 2018 - 2030)

Active Barriers

Passive Barriers

Barrier Systems Access Control Device Outlook (Revenue, USD Billion, 2018 - 2030)

Biometric Systems

Perimeter Security Systems & Alarms

Token & Reader Function

Turnstile

Others

Barrier Systems Material Outlook (Revenue, USD Billion, 2018 - 2030)

Metal

Non-metal

Barrier Systems End-use Outlook (Revenue, USD Billion, 2018 - 2030)

Commercial

Data Centers

Financial Institutions

Government

Industrial

Petrochemical

Military & Defense

Transportation

Others

Barrier Systems Regional Outlook (Revenue, USD Billion, 2018 - 2030)

North America

Canada

Europe

Germany

France

Italy

Spain

Asia Pacific

China

India

Japan

South Korea

Australia

Latin America

Brazil

Mexico

Argentina

Middle East & Africa

A.E

Saudi Arabia

South Africa

Key Players In The Barrier Systems Market

A-Safe

BOPLAN

Ritehite

Fabenco by Tractel

Lindsay Corporation

Valmont Industries Inc.

Barrier1

Betafence

Gramm Barriers

Hill & Smith PLC

CAI Safety Systems, Inc.

Kirchdorfer Industries

Tata Steel

Arbus

Avon Barrier Corporation Ltd

Order a free sample PDF of the Barrier Systems Market Intelligence Study, published by Grand View Research.

#Barrier Systems Market

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trendswerespotted · 6 months ago

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Final thoughts on synthetic actors

Personally, I think that the rise of synthetic actors is inevitable and that it will continue as fans are shocked with each passing of their favorite celebrities. The truth is, synthetic actors are already here, although they haven't been explicitly labeled as such. We've seen them in movies, in video games, and we are now seeing them posting, commenting, and interacting autonomously on social media.

Like I've mentioned before, the technology isn't the limiting factors here. It is already exceptionally advanced and will only continue getting better. Humans are the true obstacles to the ultimate implementation of synthetic actors. Either through legislation or ethical concerns, people may fear this new unknown. Creating AI models that look, sound, and act like us truly does make it feel like robots are going to take over the world and annihilate the human race. Just joking... mostly. In all seriousness, it is scary. What happens when we're not able to decipher between what's AI and what's not? This is an issue that's already happening in schools and on the internet as people spread "fake news" or use deepfakes and AI to post inaccurate/fictional scenarios (see this recent video rapper 50Cent released that uses AI to portray Jay Z and P. Diddy in handcuffs in light of the recent sexual assault allegations against Jay Z. Jay Z has not yet been arrested). With every new invention, people will find ways to abuse it, but that doesn't mean we should stop inventing. Instead, how can we create legislation and guardrails at the same pace that this technology is advancing? These AI actors are a sign of progress and open up so many opportunities for engaging with (customized) content, and may even benefit actors who have busy schedules or have gotten injured during a shoot. Maybe it can even make filmmaking more accessible to smaller studios who cannot afford the real life Dwayne Johnson, but can license his virtual twin... For the most part, I see this technology as a good thing that we should keep developing and exploring, but again, it's crucial that we dot our i's and cross our t's before simply releasing it out into the world.

#mmi634

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fronzennews · 7 months ago

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AI Threatens Humanity Dark Era Examining Current Impacts and Risks

The notion that AI could plunge humanity into a "new dark era" has ignited intense debate among experts. With a myriad of perspectives on the nature and severity of the threats posed by advanced artificial intelligence, discussions oscillate between existential risks and immediate societal harms. Understanding both aspects is critical in navigating this complex landscape.

1. Existential Risks: Could AI Endanger Humanity?

This section delves into the apocalyptic scenarios proposed by some experts regarding AI's potential existential risks. Influential figures in the tech industry and academia suggest that, if unchecked, AI may threaten human survival in ways akin to pandemics or nuclear conflict. 1.1 The Consciousness Conundrum One of the most alarming theories posits that AI could eventually gain consciousness. If this were to occur, the implications could be dire as it might lead AI to intentionally cause harm to humanity, mirroring themes found in speculative fiction. 1.2 Infrastructure Under Siege: The Control Factor In another worrisome scenario, experts highlight AI’s control over critical infrastructure. This includes sectors like energy, food supply, and transportation. A malfunction or malicious manipulation of these systems might result in catastrophic outcomes, reminiscent of disasters associated with pandemics or nuclear warfare.

Photo by Ron Lach

2. Immediate Harms: More Than Speculation

Shifting gears, this section discusses the current, tangible harms inflicted by AI systems that pose real challenges to society today, arguing that these issues warrant immediate attention. 2.1 Environmental Degradation: The Hidden Costs AI technologies are often energy-intensive, contributing to environmental degradation and climate change. The models consume significant resources, resulting in a carbon footprint that exacerbates global warming. Various studies indicate that large-scale AI systems can require as much energy as small countries. 2.2 Unconscious Bias: The Social Impact of AI Decisions AI is inherently reflective of the data it is trained on. Consequently, it can perpetuate and amplify existing social biases. This has serious ramifications, particularly in fields like law enforcement and hiring, where AI systems may contribute to unjust outcomes by misrepresenting certain demographics. 2.3 Intellectual Property Rights: The Artist's Plight Moreover, the use of copyrighted material in training AI models has raised ethical concerns. Many AI systems employ artworks and writings created by individuals without obtaining their consent, leading to a dispute over intellectual property rights in the digital space. 2.4 Surveillance Concerns: Privacy in the Age of AI AI technologies have significantly enhanced surveillance capabilities, raising important concerns regarding individual privacy and civil liberties. Governments and corporations are increasingly utilizing AI to monitor behavior and activities, often without proper oversight or accountability.

3. Regulatory and Ethical Solutions: Building Safeguards Against Dark Outcomes

This section outlines the growing calls for stringent regulations and ethical guidelines aimed at mitigating AI's harmful impact and promoting its responsible use. 3.1 Establishing Meaningful Guardrails There is an urgent need for regulatory frameworks that prioritize transparency, accountability, and alignment with human values. Various experts advocate for developing "meaningful guardrails" that can protect vulnerable populations and ensure AI is deployed responsibly. 3.2 Proposals for Best Practices in AI Development Best practices are essential for AI developers and organizations. Proposals include creating comprehensive guidelines for ethical AI development that minimizes risks and supports fair societal advancement. These practices can help ensure advancements benefit all sectors of society equitably.

Photo by Pavel Danilyuk

4. Autonomy and Unpredictability Navigating the Unknown

This section addresses the growing autonomy of AI—how these systems operate with significant decision-making capabilities and the challenges this presents for human control and understanding. 4.1 Understanding AI Decision Frameworks As AI systems evolve, understanding how they learn and make decisions becomes increasingly important. The complexities of these frameworks can pose challenges, particularly when decisions made by AI may not align with human expectations or understanding. 4.2 Ensuring Reliability and Control Ensuring the reliability and controllability of autonomous AI systems is a key concern. Experts are working on developing techniques to improve the predictability of these systems, aiming to maintain human oversight and trust throughout their operation.

5. Leadership and Adaptation: Shaping the Future

In the final section, we explore the role of leadership in adapting to the evolving landscape of AI, emphasizing the importance of ethical considerations and equitable distribution of AI's benefits and costs. 5.1 Valuing Human Skills in an AI-Driven World Despite the rapid advancement of AI technologies, human skills remain invaluable. Emphasizing the importance of fostering a balanced relationship between technology and the workforce is crucial. This can ensure that human contribution is recognized and remains integral to industries transformed by AI. 5.2 Making Conscious Choices: The Power of Human Agency Human decisions will ultimately shape the trajectory of AI development. By advocating for ethical leadership and proactive governance, stakeholders can influence the manner in which AI is integrated into society, steering its capabilities toward positive outcomes.

Navigating the AI Terrain

The discourse surrounding AI's role in potentially causing a dark era or leading to positive change is multifaceted. While some experts express concern regarding existential risks, others assert that these fears may distract from crucial, immediate issues faced by society today. Focusing on regulation, ethical practices, and the impacts of existing AI applications is essential for ensuring that this powerful technology serves humanity positively, rather than leading it into uncertainty. If you are interested in more news and insights on similar topics, I invite you to explore my blog. For the latest updates, visit FROZENLEAVES NEWS. ``` Read the full article

#A.R.#AI #Innovation #investment #Politics #Society

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govindhtech · 1 year ago

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Global AI governance exists in a complex & dynamic ecosystem

AI governance platform

The environment of global AI governance is complicated and changing quickly. Important themes and issues are starting to surface, but government organizations should be proactive and assess their priorities and procedures in-house. The last step is only to ensure that official policies are followed using auditing tools and other techniques. The foundation for successfully operationalizing governance is human-centered and consists of creating centers of excellence and agency-wide AI literacy, identifying responsible leaders, obtaining funded mandates, and combining knowledge from the public, nonprofit, and commercial sectors.

The environment of global governance The OECD Policy Observatory now maintains a list of 668 national AI governance projects from 69 nations, territories, and the European Union. These include national agendas, goals, and strategies; agencies tasked with coordinating or overseeing AI; stakeholder or expert public consultations; and projects aimed at implementing AI in the public sector. Furthermore, the OECD classifies legally binding AI standards and rules apart from the previously stated projects, including an additional 337 initiatives in this category.

It might be challenging to define the word governance. When referring to AI, it can mean government-mandated regulations, policies governing data access and model usage, or the safety and ethical boundaries of AI tools and systems themselves. As a result, observe that different national and international recommendations approach these overlapping and crossing meanings. Because of all these factors, AI governance ought to start at the conceptual stage and go on throughout the AI solution’s lifecycle.

Common issues, themes As demonstrated by the recent White House directive establishing AI governance committees in U.S. federal agencies, government agencies generally aim for governance that supports and strikes a balance between societal concerns of economic success, national security, and political dynamics. In the meantime, a lot of private businesses appear to place a higher priority on economic prosperity, emphasising productivity and efficiency as the keys to both business success and shareholder value. Some businesses, like IBM, place particular emphasis on incorporating guardrails into AI workflows.

Academics, non-governmental organisations, and other specialists are also issuing guidelines that are helpful to public sector institutions. The Presidio AI Framework (PDF) was released this year by the AI Governance Alliance of the World Economic Forum. It “provides a secure way to create, apply, and use generative AI.” The framework highlights safety problem-solving opportunities and gaps. from the viewpoints of four main actors: consumers of AI applications, authors of AI models, adapters of AI models, and users of AI models.

Certain regulatory themes are emerging that are common to many businesses and sectors. For example, it is becoming more and more prudent to inform end users about the existence and purpose of any AI they are using. Leaders need to guarantee consistency in output, defence against criticism, and a practical dedication to social responsibility. Fairness and objectivity in training data and output should be prioritised, environmental effect should be kept to a minimum, and accountability should be raised through organization-wide education and the designation of accountable persons.

Policies alone are insufficient Regardless of whether they are drafted with rigour or comprehensiveness, governance policies are merely guidelines regardless of whether they are enforced formally or through soft law. What matters is how organisations implement them. As an illustration, New York City formalised its AI principles in March 2024 and released its own AI Action plan in October 2023. These guidelines supported the aforementioned themes, such as the idea that AI technologies “should be tested before deployment,” but they also encouraged people to disobey the law. This was the case with the AI-powered chatbot the city implemented to respond to inquiries regarding opening and running a business. Where did the execution go wrong?

A participative, responsible, and human-centered approach is necessary for operationalizing government. Let’s examine the three crucial steps that organizations need to do:

Name responsible leaders and provide the resources they need to carry out their duties Accountability is necessary for trust to exist. Government agencies need accountable executives who are mandated by funding to operationalize governance structures. To provide just one example of a knowledge gap, we’ve spoken with a number of senior technology professionals who are unaware of the possibility of data bias. Data is a product of the human experience and can solidify injustice and worldviews. AI might be thought of as a mirror reflecting back to us their own prejudices. They must find responsible leaders who grasp this and who can be held accountable for making sure their AI is run ethically and in line with the values of the community it serves, in addition to providing them with financial support.

Offer instruction in applied governance Numerous organisations are hosting hackathons and AI “innovation days” with the goal of increasing operational efficiencies (i.e., cutting expenses, involving citizens or staff, and other KPIs). They suggest expanding the scope of these hackathons to tackle the difficulties associated with AI governance by taking the following actions:

Step 1 Have a prospective governance leader give a keynote address on AI ethics to hackathon attendees three months prior to the pilots’ presentation.

Step 2 Assign the role of event judge to the government agency creating the policy. Give criteria for evaluating pilot projects that take into account the functional and non-functional needs of the model being used, as well as AI governance artefacts (documentation outputs) such as factsheets, audit reports, and layers-of-effect analyses (intended, unintended, primary, and secondary impacts).

Step 3 Provide the teams with applicable training on creating these artefacts through workshops based on their individual use cases for six to eight weeks prior to the presentation date. Encourage diversified, multidisciplinary teams to participate in these workshops with development teams to help them evaluate ethics and predict risk.

Step 4 Have each team present their work holistically on the day of the event, showing how they have evaluated and would reduce different risks related to their use cases. Each team’s work should be questioned and assessed by judges with credentials in cybersecurity, regulation, and domain expertise.

Based on ibm expertise providing practitioners with applicable training related to highly specific use cases, these timetables have been developed. It places team members in the position of astute governance judges while allowing aspiring leaders the opportunity to carry out the actual task of governance under the supervision of a coach.

However, hackathons are insufficient. In three months, one cannot learn everything. Agencies should make the investment to create an AI literacy education culture that encourages lifelong learning, including the occasional rejection of preconceived notions.

Assess inventory using methods other than algorithmic impact analyses Algorithmic impact assessment forms are widely used by organisations that create a large number of AI models as their main tool for collecting pertinent inventory metadata and evaluating and reducing the risks associated with AI models prior to deployment. These forms merely ask about the AI model’s goal, training data and methodology, responsible parties, and concerns over uneven impact. They do not ask questions about AI model owners or procurers.

The employment of these forms in isolation without rigorous education, communication, and cultural considerations raises a number of concerns. Among them are:

Rewards Are people encouraged or discouraged from carefully completing these forms? discover that most are under pressure to reach quotas, which disincentivizes them.

Acceptance of risk These documents may suggest that because a model was obtained from a third party or that they utilised a specific technology or cloud host, the model owners will be released from liability.

Relevant AI definitions It’s possible that model owners are unaware that what they are installing or acquiring fits the regulatory definition of intelligent automation, or AI.

Ignorance of the varying effects One could argue that an accurate assessment of differential impact is intentionally removed by placing the burden of completion and submission of an algorithmic assessment form on a single individual.

Ibm have seen alarming form submissions from AI practitioners who claim to have read the published policy and comprehend its ideas, as well as those from a variety of educational backgrounds and geographic locations. These entries include things like “There are no risks for disparate impact as I have the best of intentions,” and “How could my AI model be unfair if I am not gathering PII.” These highlight the pressing need for practical training and a corporate culture that regularly compares model conduct to well-defined ethical standards.

Fostering a collaborative and accountable culture As organisations struggle to manage a technology that has such a broad influence, a participatory and inclusive culture is crucial. Diversity is a mathematical factor, not a political one, as ibm have previously discussed. Multidisciplinary centres of excellence play a crucial role in ensuring that staff members are knowledgeable, accountable AI users who are aware of the dangers and varying effects. Organisations should emphasise that everyone bears accountability, not only model owners, and integrate governance into collaborative innovation initiatives. They need to find really responsible leaders who address governance problems from a socio-technical standpoint and who are open to new ideas for reducing the risk associated with AI, whether they come from governmental, non-governmental, or academic sources.