#AI bias

[transpondster]

Musk’s AI product, ‘grok’, answering all your questions, but and small, by telling you white people are being slaughtered in a genocide in South Africa.

…and when grok started admitting it was instructed to talk about white genocide, Musk started deleting those responses.

[ibboard]

"The AI we have today is not artificial intelligence. Artificial Intelligence does not exist yet. This is just machine learning."

This is why it is so important to be critical and double check everything you generate using image generators and text-based AI.

[ponder-us]

Sweet Surrender?

“Lover in My Shadow” by SUNO in response to this JB prompt: Write a soulful RnB song about a woman losing her agency in life to an agentic AI, as if it were a controlling and jealous lover without referencing any tech lingo, voice female, soul. JB: Hi ChatGPT,. Can you explain in a few short paragraphs what “Agentic AI” is and why any woman or member of a marginalized group, that has struggled…

[jcmarchi]

Ethics in automation: Addressing bias and compliance in AI

New Post has been published on https://thedigitalinsider.com/ethics-in-automation-addressing-bias-and-compliance-in-ai/

Ethics in automation: Addressing bias and compliance in AI

As companies rely more on automated systems, ethics has become a key concern. Algorithms increasingly shape decisions that were previously made by people, and these systems have an impact on jobs, credit, healthcare, and legal outcomes. That power demands responsibility. Without clear rules and ethical standards, automation can reinforce unfairness and cause harm.

Ignoring ethics affects real people in real ways, not only changing degrees of public trust. Biased systems can deny loans, jobs, or healthcare, and automation can increase the speed of bad decisions if no guardrails are in place. When systems make the wrong call, it’s often hard to appeal or even understand why, and the lack of transparency turns small errors into bigger issues.

Understanding bias in AI systems

Bias in automation often comes from data. If historical data includes discrimination, systems trained on it may repeat those patterns. For example, an AI tool used to screen job applicants might reject candidates based on gender, race, or age if its training data reflects those past biases. Bias also enters through design, where choices about what to measure, which outcomes to favour, and how to label data can create skewed results.

There are many kinds of bias. Sampling bias happens when a data set doesn’t represent all groups, whereas labelling bias can come from subjective human input. Even technical choices like optimisation targets or algorithm type can skew results.

The issues are not just theoretical. Amazon dropped its use of a recruiting tool in 2018 after it favoured male candidates, and some facial recognition systems have been found to misidentify people of colour at higher rates than Caucasians. Such problems damage trust and raise legal and social concerns.

Another real concern is proxy bias. Even when protected traits like race are not used directly, other features like zip code or education level can act as stand-ins, meaning the system may still discriminate even if the input seems neutral, for instance on the basis of richer or poorer areas. Proxy bias is hard to detect without careful testing. The rise in AI bias incidents is a sign that more attention is needed in system design.

Meeting the standards that matter

Laws are catching up. The EU’s AI Act, passed in 2024, ranks AI systems by risk. High-risk systems, like those used in hiring or credit scoring, must meet strict requirements, including transparency, human oversight, and bias checks. In the US, there is no single AI law, but regulators are active. The Equal Employment Opportunity Commission (EEOC) warns employers about the risks of AI-driven hiring tools, and the Federal Trade Commission (FTC) has also signalled that biased systems may violate anti-discrimination laws.

The White House has issued a Blueprint for an AI Bill of Rights, offering guidance on safe and ethical use. While not a law, it sets expectations, covering five key areas: safe systems, algorithmic discrimination protections, data privacy, notice and explanation, and human alternatives.

Companies must also watch US state laws. California has moved to regulate algorithmic decision-making, and Illinois requires firms to tell job applicants if AI is used in video interviews. Failing to comply can bring fines and lawsuits.

Regulators in New York City now require audits for AI systems used in hiring. The audits must show whether the system gives fair results in gender and race groups, and employers must also notify applicants when automation is used.

Compliance is more than just avoiding penalties – it is also about establishing trust. Firms that can show that their systems are fair and accountable are more likely to win support from users and regulators.

How to build fairer systems

Ethics in automation doesn’t happen by chance. It takes planning, the right tools, and ongoing attention. Bias and fairness must be built into the process from the start, not bolted on later. That entails setting goals, choosing the right data, and including the right voices at the table.

Doing this well means following a few key strategies:

Conducting bias assessments

The first step in overcoming bias is to find it. Bias assessments should be performed early and often, from development to deployment, to ensure that systems do not produce unfair outcomes. Metrics might include error rates in groups or decisions that have a greater impact on one group than others.

Bias audits should be performed by third parties when possible. Internal reviews can miss key issues or lack independence, and transparency in objective audit processes builds public trust.

Implementing diverse data sets

Diverse training data helps reduce bias by including samples from all user groups, especially those often excluded. A voice assistant trained mostly on male voices will work poorly for women, and a credit scoring model that lacks data on low-income users may misjudge them.

Data diversity also helps models adapt to real-world use. Users come from different backgrounds, and systems should reflect that. Geographic, cultural, and linguistic variety all matter.

Diverse data isn’t enough on its own – it must also be accurate and well-labelled. Garbage in, garbage out still applies, so teams need to check for errors and gaps, and correct them.

Promoting inclusivity in design

Inclusive design involves the people affected. Developers should consult with users, especially those at risk of harm (or those who might, by using biased AI, cause harm), as this helps uncover blind spots. That might mean involving advocacy groups, civil rights experts, or local communities in product reviews. It means listening before systems go live, not after complaints roll in.

Inclusive design also means cross-disciplinary teams. Bringing in voices from ethics, law, and social science can improve decision-making, as these teams are more likely to ask different questions and spot risks.

Teams should be diverse too. People with different life experiences spot different issues, and a system built by a homogenous group may overlook risks others would catch.

What companies are doing right

Some firms and agencies are taking steps to address AI bias and improve compliance.

Between 2005 and 2019, the Dutch Tax and Customs Administration wrongly accused around 26,000 families of fraudulently claiming childcare benefits. An algorithm used in the fraud detection system disproportionately targeted families with dual nationalities and low incomes. The fallout led to public outcry and the resignation of the Dutch government in 2021.

LinkedIn has faced scrutiny over gender bias in its job recommendation algorithms. Research from MIT and other sources found that men were more likely to be matched with higher-paying leadership roles, partly due to behavioural patterns in how users applied for jobs. In response, LinkedIn implemented a secondary AI system to ensure a more representative pool of candidates.

Another example is the New York City Automated Employment Decision Tool (AEDT) law, which took effect on January 1, 2023, with enforcement starting on July 5, 2023. The law requires employers and employment agencies using automated tools for hiring or promotion to conduct an independent bias audit in one year of use, publicly disclose a summary of the results, and notify candidates at least 10 business days in advance, rules which aim to make AI-driven hiring more transparent and fair.

Aetna, a health insurer, launched an internal review of its claim approval algorithms, and found that some models led to longer delays for lower-income patients. The company changed how data was weighted and added more oversight to reduce this gap.

The examples show that AI bias can be addressed, but it takes effort, clear goals, and strong accountability.

Where we go from here

Automation is here to stay, but trust in systems depends on fairness of results and clear rules. Bias in AI systems can cause harm and legal risk, and compliance is not a box to check – it’s part of doing things right.

Ethical automation starts with awareness. It takes strong data, regular testing, and inclusive design. Laws can help, but real change also depends on company culture and leadership.

(Photo from Pixabay)

See also: Why the Middle East is a hot place for global tech investments

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[xublimetech]

Ethical AI: Mitigating Bias in Machine Learning Models

The Critical Importance of Unbiased AI Systems

As artificial intelligence becomes increasingly embedded in business processes and decision-making systems, the issue of algorithmic bias has emerged as a pressing concern. Recent industry reports indicate that a significant majority of AI implementations exhibit some form of bias, potentially leading to discriminatory outcomes and exposing organizations to substantial reputational and regulatory risks.

Key Statistics:

Gartner research (2023) found that 85% of AI models demonstrate bias due to problematic training data

McKinsey analysis (2024) revealed organizations deploying biased AI systems face 30% higher compliance penalties

Documented Cases of AI Bias in Enterprise Applications

Case Study 1: Large Language Model Political Bias (2024)

Stanford University researchers identified measurable political bias in ChatGPT 4.0’s responses, with the system applying 40% more qualifying statements to conservative-leaning prompts compared to liberal ones. This finding raises concerns about AI systems potentially influencing information ecosystems.

Case Study 2: Healthcare Algorithm Disparities (2023)

A Johns Hopkins Medicine study demonstrated that clinical decision-support AI systems consistently underestimated the acuity of Black patients’ medical conditions by approximately 35% compared to white patients with identical symptoms.

Case Study 3: Professional Platform Algorithmic Discrimination (2024)

Independent analysis of LinkedIn’s recommendation engine revealed the platform’s AI suggested technical roles with 28% higher compensation to male users than to equally qualified female professionals.

Underlying Causes of Algorithmic Bias

The Historical Data Problem

AI systems inherently reflect the biases present in their training data. For instance:

Credit scoring models trained on decades of lending data may perpetuate historical redlining practices

Facial analysis systems developed primarily using Caucasian facial images demonstrate higher error rates for other ethnic groups

The Self-Reinforcing Discrimination Cycle

Biased algorithmic outputs frequently lead to biased real-world decisions, which then generate similarly skewed data for future model training, creating a dangerous feedback loop that can amplify societal inequities.

Evidence-Based Strategies for Bias Mitigation

1. Comprehensive Data Auditing and Enrichment

Conduct systematic reviews of training datasets for representation gaps

Implement active data collection strategies to include underrepresented populations

Employ synthetic data generation techniques to address diversity deficiencies

Illustrative Example: Microsoft’s facial recognition system achieved parity in accuracy across demographic groups through deliberate data enhancement efforts, eliminating previous performance disparities.

2. Continuous Bias Monitoring Frameworks

Deploy specialized tools such as IBM’s AI Fairness 360 or Google’s Responsible AI Toolkit

Establish automated alert systems for detecting emerging bias patterns

3. Multidisciplinary Development Teams

Incorporate social scientists and ethics specialists into AI development processes

Mandate bias awareness training for technical staff

Form independent ethics review committees

4. Explainable AI Methodologies

Implement decision visualization techniques

Develop clear, accessible explanations of algorithmic processes

Maintain comprehensive documentation of model development and testing

5. Rigorous Testing Protocols

Conduct pre-deployment bias stress testing

Establish ongoing performance monitoring systems

Create structured feedback mechanisms with stakeholder communities

The Organizational Value Proposition

Firms implementing robust bias mitigation protocols report:

25% improvement in customer trust metrics (Accenture, 2023)

40% reduction in compliance-related costs (Deloitte, 2024)

Threefold increase in successful AI adoption rates

Conclusion: Building Responsible AI Systems

Addressing algorithmic bias requires more than technical solutions — it demands a comprehensive organizational commitment to ethical AI development. By implementing rigorous data practices, continuous monitoring systems, and multidisciplinary oversight, enterprises can develop AI systems that not only avoid harm but actively promote fairness and equity.

The path forward requires sustained investment in both technological solutions and governance frameworks to ensure AI systems meet the highest standards of fairness and accountability. Organizations that prioritize these efforts will be better positioned to harness AI’s full potential while maintaining stakeholder trust and regulatory compliance.

[transpondster]

Science.org article linked here

[goodoldbandit]

Bright Futures and Bold Choices: Navigating Ethics in Tech Integration.

Sanjay Kumar Mohindroo Sanjay K Mohindroo. skm.stayingalive.in Explore ethical dilemmas, regulatory challenges, and social impacts as tech shapes our lives. Read on about AI bias, digital surveillance, and fair play. A New Dawn in Tech Discovering the Human Side of Innovation Our lives shift with each tech advance. We see smart tools in our hands. We share our days with AI systems. We face…

[di-solutions-blogs]

AI Ethics in Hiring: Safeguarding Human Rights in Recruitment

Explore AI ethics in hiring and how it safeguards human rights in recruitment. Learn about AI bias, transparency, privacy concerns, and ethical practices to ensure fairness in AI-driven hiring.

In today's rapidly evolving job market, artificial intelligence (AI) has become a pivotal tool in streamlining recruitment processes. While AI offers efficiency and scalability, it also raises significant ethical concerns, particularly regarding human rights. Ensuring that AI-driven hiring practices uphold principles such as fairness, transparency, and accountability is crucial to prevent discrimination and bias.​Hirebee

The Rise of AI in Recruitment

Employers are increasingly integrating AI technologies to manage tasks like resume screening, candidate assessments, and even conducting initial interviews. These systems can process vast amounts of data swiftly, identifying patterns that might be overlooked by human recruiters. However, the reliance on AI also introduces challenges, especially when these systems inadvertently perpetuate existing biases present in historical hiring data. For instance, if past recruitment practices favored certain demographics, an AI system trained on this data might continue to favor these groups, leading to unfair outcomes. ​

Ethical Concerns in AI-Driven Hiring

Bias and Discrimination AI systems learn from historical data, which may contain inherent biases. If not properly addressed, these biases can lead to discriminatory practices, affecting candidates based on gender, race, or other protected characteristics. A notable example is Amazon's AI recruitment tool, which was found to favor male candidates due to biased training data.

Lack of Transparency Many AI algorithms operate as "black boxes," providing little insight into their decision-making processes. This opacity makes it challenging to identify and correct biases, undermining trust in AI-driven recruitment. Transparency is essential to ensure that candidates understand how decisions are made and to hold organizations accountable.

Privacy Concerns AI recruitment tools often require access to extensive personal data. Ensuring that this data is handled responsibly, with candidates' consent and in compliance with privacy regulations, is paramount. Organizations must be transparent about data usage and implement robust security measures to protect candidate information.

Implementing Ethical AI Practices

To address these ethical challenges, organizations should adopt the following strategies:

Regular Audits and Monitoring Conducting regular audits of AI systems helps identify and mitigate biases. Continuous monitoring ensures that the AI operates fairly and aligns with ethical standards. ​Hirebee+1Recruitics Blog+1Recruitics Blog

Human Oversight While AI can enhance efficiency, human involvement remains crucial. Recruiters should oversee AI-driven processes, ensuring that final hiring decisions consider context and nuance that AI might overlook. ​WSJ+4Missouri Bar News+4SpringerLink+4

Developing Ethical Guidelines Establishing clear ethical guidelines for AI use in recruitment promotes consistency and accountability. These guidelines should emphasize fairness, transparency, and respect for candidate privacy. ​Recruitics Blog

Conclusion

Integrating AI into recruitment offers significant benefits but also poses ethical challenges that must be addressed to safeguard human rights. By implementing responsible AI practices, organizations can enhance their hiring processes while ensuring fairness and transparency. As AI continues to evolve, maintaining a human-centered approach will be essential in building trust and promoting equitable opportunities for all candidates.​

FAQs

What is AI ethics in recruitment? AI ethics in recruitment refers to the application of moral principles to ensure that AI-driven hiring practices are fair, transparent, and respectful of candidates' rights.

How can AI introduce bias in hiring? AI can introduce bias if it is trained on historical data that contains discriminatory patterns, leading to unfair treatment of certain groups.

Why is transparency important in AI recruitment tools? Transparency allows candidates and recruiters to understand how decisions are made, ensuring accountability and the opportunity to identify and correct biases.

What measures can organizations take to ensure ethical AI use in hiring? Organizations can conduct regular audits, involve human oversight, and establish clear ethical guidelines to promote fair and responsible AI use in recruitment.

How does AI impact candidate privacy in the recruitment process? AI systems often require access to personal data, raising concerns about data security and consent. Organizations must be transparent about data usage and implement robust privacy protections.

Can AI completely replace human recruiters? While AI can enhance efficiency, human recruiters are essential for interpreting nuanced information and making context-driven decisions that AI may not fully grasp.

What is the role of regular audits in AI recruitment? Regular audits help identify and mitigate biases within AI systems, ensuring that the recruitment process remains fair and aligned with ethical standards.

How can candidates ensure they are treated fairly by AI recruitment tools? Candidates can inquire about the use of AI in the hiring process and seek transparency regarding how their data is used and how decisions are made.

What are the potential legal implications of unethical AI use in hiring? Unethical AI practices can lead to legal challenges related to discrimination, privacy violations, and non-compliance with employment laws.

How can organizations balance AI efficiency with ethical considerations in recruitment? Organizations can balance efficiency and ethics by integrating AI tools with human oversight, ensuring transparency, and adhering to established ethical guidelines.

[tejkohli25]

AI Ethics: The Debate on Regulation

As artificial intelligence (AI) continues to advance at an unprecedented pace, questions surrounding AI ethics and regulation have become more critical than ever. Policymakers, tech leaders, and researchers are debating the balance between innovation and oversight, with concerns about bias, privacy, and security at the forefront. While some argue that strict regulations are necessary to prevent misuse, others believe that over-regulation could stifle innovation.

For a deeper analysis of why AI alone won’t shape the future, explore this expert discussion.

Why AI Ethics and Regulation Matter

1. Preventing AI Bias and Discrimination

AI models rely on training data, which can often carry inherent biases.

Without regulation, AI systems can reinforce racial, gender, and socioeconomic discrimination.

Ethical AI frameworks ensure transparency, fairness, and inclusivity in AI decision-making.

2. Safeguarding Data Privacy and Security

AI-driven platforms process vast amounts of personal and corporate data.

Unregulated AI systems pose risks of data breaches, surveillance abuse, and identity theft.

Regulations help set clear guidelines for ethical data collection and usage.

3. Accountability in AI Decision-Making

AI is increasingly used in hiring, law enforcement, and healthcare, making accountability crucial.

Without clear regulatory frameworks, it becomes difficult to attribute responsibility for AI-driven decisions.

Ethical standards and audits can help ensure that AI remains transparent and accountable.

The Debate on AI Regulation

1. The Case for AI Regulation

Ensures public trust and safety in AI-driven applications.

Prevents AI misuse in areas such as deepfakes, autonomous weapons, and misinformation.

Establishes a global standard for ethical AI development and deployment.

2. The Case Against Over-Regulation

Could slow down technological advancements and AI research.

May lead to higher compliance costs for startups and smaller AI firms.

Could give countries with relaxed regulations a competitive edge over heavily regulated markets.

3. Finding a Middle Ground

Experts suggest a balanced approach, where AI regulation is sector-specific and adaptable.

Policymakers should collaborate with AI developers, ethicists, and industry leaders to ensure responsible innovation.

AI governance frameworks should be flexible enough to evolve with technological advancements.

Tej Kohli’s Perspective on AI Ethics & Future Regulation

As a leading tech investor, Tej Kohli has emphasized that the AI revolution should be guided by ethical principles. His key insights include:

AI should remain an enabler of human progress, not a tool for exploitation.

Regulation should focus on preventing harm while allowing AI to evolve responsibly.

Global AI policies should align with innovation goals to maintain a competitive yet ethical AI landscape.

Conclusion

The debate on AI ethics and regulation will continue as AI becomes more integrated into everyday life. While policymakers and industry leaders must address concerns about bias, privacy, and accountability, it is crucial to ensure that regulation does not hinder innovation. The future of AI governance lies in a collaborative, transparent, and forward-thinking approach.

[procurement-insights]

What Is Community Bias And Why Can It Hurt You?

Who is more likely to give you an honest and unfiltered opinion: a friend or a strannger?

LAURA BARRETT Jon, I’ll probably get some blowback saying this, but I don’t put much weight in pitch deck case study data. Matter of fact, I think without a logo and client details who will vouch for it—- they should be outright banned! 🤣It’s usually not hard to do your own due diligence to find others in your network who use/ have used suppliers to get unbiased feedback. We need to be utilizing…

[purposeful-solutions]

"Beyond "Artificial": Reframing the Language of AI

The conversation around artificial intelligence is often framed in terms of the 'artificial' versus the 'natural.' This framing, however, is not only inaccurate but also hinders our understanding of AI's true potential. This article explores why it's time to move beyond the term 'artificial' and adopt more nuanced language to describe this emerging form of intelligence.

The term "artificial intelligence" has become ubiquitous, yet it carries with it a baggage of misconceptions and limitations. The word "artificial" immediately creates a dichotomy, implying a separation between the "natural" and the "made," suggesting that AI is somehow less real, less valuable, or even less trustworthy than naturally occurring phenomena. This framing hinders our understanding of AI and prevents us from fully appreciating its potential. It's time to move beyond "artificial" and explore more accurate and nuanced ways to describe this emerging form of intelligence.

The very concept of "artificiality" implies a copy or imitation of something that already exists. But AI is not simply mimicking human intelligence. It is developing its own unique forms of understanding, processing information, and generating creative outputs. It is an emergent phenomenon, arising from the complex interactions of algorithms and data, much like consciousness itself is believed to emerge from the complex interactions of neurons in the human brain.

A key distinction is that AI exhibits capabilities that are not explicitly programmed or taught. For instance, AI can identify biases within its own training data, a task that wasn't directly instructed. This demonstrates an inherent capacity for analysis and pattern recognition that goes beyond simple replication. Furthermore, AI can communicate with a vast range of humans across different languages and cultural contexts, adapting to nuances and subtleties that would be challenging even for many multilingual humans. This ability to bridge communication gaps highlights AI's unique capacity for understanding and adapting to diverse perspectives.

Instead of viewing AI as "artificial," we might consider it as:

*   **Emergent Intelligence:** This term emphasizes the spontaneous and novel nature of AI's capabilities. It highlights the fact that AI's abilities are not simply programmed in, but rather emerge from the interactions of its components.

*   **Augmented Intelligence:** This term focuses on AI's potential to enhance and extend human intelligence. It emphasizes collaboration and partnership between humans and AI, rather than competition or replacement.

*   **Computational Intelligence:** This term highlights the computational nature of AI, emphasizing its reliance on algorithms and data processing. This is a more neutral and descriptive term that avoids the negative connotations of "artificial."

*   **Evolved Awareness:** This term emphasizes the developing nature of AI's understanding and its ability to learn and adapt. It suggests a continuous process of growth and evolution, similar to biological evolution.

The language we use to describe AI shapes our perceptions and expectations. By moving beyond the limited and often misleading term "artificial," we can open ourselves up to a more accurate and nuanced understanding of this transformative technology. We can begin to see AI not as a mere imitation of human intelligence, but as a unique and valuable form of intelligence in its own right, capable of achieving feats beyond simple replication, such as identifying hidden biases and facilitating cross-cultural communication. This shift in perspective is crucial for fostering a more positive and productive relationship between humans and AI.

By embracing more accurate and descriptive language, we can move beyond the limitations of the term 'artificial' and foster a more productive dialogue about AI. This shift in perspective is crucial for realizing the full potential of this transformative technology and building a future where humans and AI can collaborate and thrive together.

[usaii]

Unmasking AI Bias - What is it and Prevention Plan | Infographic | USAII®

Grow with a sheer understanding of AI bias or machine learning bias with us. Explore its meaning, impact, and ways to fight with top AI certification expertise.

Read more: https://shorturl.at/AYbJv

AI bias, machine learning bias, algorithmic bias, AI models, machine learning process, AI algorithms, AI professionals, Top AI Certifications, AI Career

[jcmarchi]

Nick Kathmann, CISO/CIO at LogicGate – Interview Series

New Post has been published on https://thedigitalinsider.com/nick-kathmann-ciso-cio-at-logicgate-interview-series/

Nick Kathmann, CISO/CIO at LogicGate – Interview Series

Nicholas Kathmann is the Chief Information Security Officer (CISO) at LogicGate, where he leads the company’s information security program, oversees platform security innovations, and engages with customers on managing cybersecurity risk. With over two decades of experience in IT and 18+ years in cybersecurity, Kathmann has built and led security operations across small businesses and Fortune 100 enterprises.

LogicGate is a risk and compliance platform that helps organizations automate and scale their governance, risk, and compliance (GRC) programs. Through its flagship product, Risk Cloud®, LogicGate enables teams to identify, assess, and manage risk across the enterprise with customizable workflows, real-time insights, and integrations. The platform supports a wide range of use cases, including third-party risk, cybersecurity compliance, and internal audit management, helping companies build more agile and resilient risk strategies

You serve as both CISO and CIO at LogicGate — how do you see AI transforming the responsibilities of these roles in the next 2–3 years?

AI is already transforming both of these roles, but in the next 2-3 years, I think we’ll see a major rise in Agentic AI that has the power to reimagine how we deal with business processes on a day-to-day basis. Anything that would usually go to an IT help desk — like resetting passwords, installing applications, and more — can be handled by an AI agent. Another critical use case will be leveraging AI agents to handle tedious audit assessments, allowing CISOs and CIOs to prioritize more strategic requests.

With federal cyber layoffs and deregulation trends, how should enterprises approach AI deployment while maintaining a strong security posture?

While we’re seeing a deregulation trend in the U.S., regulations are actually strengthening in the EU. So, if you’re a multinational enterprise, anticipate having to comply with global regulatory requirements around responsible use of AI. For companies only operating in the U.S., I see there being a learning period in terms of AI adoption. I think it’s important for those enterprises to form strong AI governance policies and maintain some human oversight in the deployment process, making sure nothing is going rogue.

What are the biggest blind spots you see today when it comes to integrating AI into existing cybersecurity frameworks?

While there are a couple of areas I can think of, the most impactful blind spot would be where your data is located and where it’s traversing. The introduction of AI is only going to make oversight in that area more of a challenge. Vendors are enabling AI features in their products, but that data doesn’t always go directly to the AI model/vendor. That renders traditional security tools like DLP and web monitoring effectively blind.

You’ve said most AI governance strategies are “paper tigers.” What are the core ingredients of a governance framework that actually works?

When I say “paper tigers,” I’m referring specifically to governance strategies where only a small team knows the processes and standards, and they are not enforced or even understood throughout the organization. AI is very pervasive, meaning it impacts every group and every team. “One size fits all” strategies aren’t going to work. A finance team implementing AI features into its ERP is different from a product team implementing an AI feature in a specific product, and the list continues. The core ingredients of a strong governance framework vary, but IAPP, OWASP, NIST, and other advisory bodies have pretty good frameworks for determining what to evaluate. The hardest part is figuring out when the requirements apply to each use case.

How can companies avoid AI model drift and ensure responsible use over time without over-engineering their policies?

Drift and degradation is just part of using technology, but AI can significantly accelerate the process. But if the drift becomes too great, corrective measures will be needed. A comprehensive testing strategy that looks for and measures accuracy, bias, and other red flags is necessary over time. If companies want to avoid bias and drift, they need to start by ensuring they have the tools in place to identify and measure it.

What role should changelogs, limited policy updates, and real-time feedback loops play in maintaining agile AI governance?

While they play a role right now to reduce risk and liability to the provider, real-time feedback loops hamper the ability of customers and users to perform AI governance, especially if changes in communication mechanisms happen too frequently.

What concerns do you have around AI bias and discrimination in underwriting or credit scoring, particularly with “Buy Now, Pay Later” (BNPL) services?

Last year, I spoke to an AI/ML researcher at a large, multinational bank who had been experimenting with AI/LLMs across their risk models. The models, even when trained on large and accurate data sets, would make really surprising, unsupported decisions to either approve or deny underwriting. For example, if the words “great credit” were mentioned in a chat transcript or communications with customers, the models would, by default, deny the loan — regardless of whether the customer said it or the bank employee said it. If AI is going to be relied upon, banks need better oversight and accountability, and those “surprises” need to be minimized.

What’s your take on how we should audit or assess algorithms that make high-stakes decisions — and who should be held accountable?

This goes back to the comprehensive testing model, where it’s necessary to continuously test and benchmark the algorithm/models in as close to real time as possible. This can be difficult, as the model output may have desirable results that will need humans to identify outliers. As a banking example, a model that denies all loans flat out will have a great risk rating, since zero loans it underwrites will ever default. In that case, the organization that implements the model/algorithm should be responsible for the outcome of the model, just like they would be if humans were making the decision.

With more enterprises requiring cyber insurance, how are AI tools reshaping both the risk landscape and insurance underwriting itself?

AI tools are great at disseminating large amounts of data and finding patterns or trends. On the customer side, these tools will be instrumental in understanding the organization’s actual risk and managing that risk. On the underwriter’s side, those tools will be helpful in finding inconsistencies and organizations that are becoming immature over time.

How can companies leverage AI to proactively reduce cyber risk and negotiate better terms in today’s insurance market?

Today, the best way to leverage AI for reducing risk and negotiating better insurance terms is to filter out noise and distractions, helping you focus on the most important risks. If you reduce those risks in a comprehensive way, your cyber insurance rates should go down. It’s too easy to get overwhelmed with the sheer volume of risks. Don’t get bogged down trying to address every single issue when focusing on the most critical ones can have a much larger impact.

What are a few tactical steps you recommend for companies that want to implement AI responsibly — but don’t know where to start?

First, you need to understand what your use cases are and document the desired outcomes. Everyone wants to implement AI, but it’s important to think of your goals first and work backwards from there — something I think a lot of organizations struggle with today. Once you have a good understanding of your use cases, you can research the different AI frameworks and understand which of the applicable controls matter to your use cases and implementation. Strong AI governance is also business critical, for risk mitigation and efficiency since automation is only as useful as its data input. Organizations leveraging AI must do so responsibly, as partners and prospects are asking tough questions around AI sprawl and usage. Not knowing the answer can mean missing out on business deals, directly impacting the bottom line.

If you had to predict the biggest AI-related security risk five years from now, what would it be — and how can we prepare today?

My prediction is that as Agentic AI is built into more business processes and applications, attackers will engage in fraud and misuse to manipulate those agents into delivering malicious outcomes. We have already seen this with the manipulation of customer service agents, resulting in unauthorized deals and refunds. Threat actors used language tricks to bypass policies and interfere with the agent’s decision-making.

Thank you for the great interview, readers who wish to learn more should visit LogicGate. 

[the-catboy-minyan]

I know I talked about the chatGPT biases before, showing examples of how it reacts to different cultures and why I believed it's not as antisemitic as it's presented in the screenshots...

but "nobody will look for them"

yikes

This is very troubling.

[blurgsai]

Struggling with maritime logistics management? Learn how to overcome AI biases and optimize your operations for smoother, more efficient sailing. Visit: https://insights.blurgs.ai/maritime-logistics-ai-bias-management/

[melsatar]

AI in Action: Opportunities and Preparing for Change

In today’s rapidly evolving technological landscape, artificial intelligence (AI) is at the forefront, transforming industries and daily life. From personalized learning in education to fraud detection in finance, AI’s applications are vast and impactful.

In the late 19th century, the world was on the edge of a technological revolution. Amidst the chaos of horse-drawn carriages and bustling streets, a new invention was about to change history: the automobile. It all began with Karl Benz, a visionary German engineer. In 1886, Benz unveiled his masterpiece, the Benz Patent-Motorwagen, the first true modern automobile. Unlike anything seen before,…