#AI bias mitigation

[techtoio]

How Artificial Intelligence is Transforming Scientific Research

Introduction No one ever imagined how artificial intelligence would revolutionize scientific research. At TechtoIO, we look into how AI is not just a tool but the driver behind the rapid advancements in many scientific disciplines. That includes how science is being transformed—from better data analysis to catalyzing discovery, such as areas in health, climate science, physics, particle experimentation, and more. Read to continue link...

[mehmetyildizmelbourne-blog]

Beware of Cognitive Biases in Generative AI Tools as a Reader, Researcher, or Reporter

Understanding How Human and Algorithmic Biases Shape Artificial Intelligence Outputs and What Users Can Do to Manage Them I have spent over 40 years studying human and machine cognition long before AI reached its current state of remarkable capabilities. Today, AI is leading us into uncharted territories. As a researcher focused on the ethical aspects of technology, I believe it is vital to…

[thisisgraeme]

Empowering Educators: Harnessing AI in Education with ChatGPT

🔍 Curious about the impact of AI in education? Discover how ChatGPT can revolutionise your teaching practices! 📚✨ Learn practical steps to enhance student engagement, ensure data privacy, and mitigate biases. Empower your classroom with cutting-edge tec

Embracing Technological Advancements in Education In the rapidly evolving landscape of education, staying abreast of technological advancements is essential for fostering dynamic and engaging learning environments. Artificial Intelligence (AI) stands at the forefront of these innovations, offering powerful tools that can transform educational practices. Among these tools, ChatGPT, an AI…

[krissym72]

Mastering the Art of Crafting AI Image Prompts: A Comprehensive Guide

In the dynamic landscape of artificial intelligence (AI), the fusion of technology and creativity has birthed a remarkable phenomenon: AI image prompts. These prompts serve as catalysts for AI systems to generate visual content autonomously, igniting a revolution in creative AI applications. Defining AI Image Prompts:AI image prompts are carefully crafted instructions or stimuli designed to…

[mariacallous]

Margaret Mitchell is a pioneer when it comes to testing generative AI tools for bias. She founded the Ethical AI team at Google, alongside another well-known researcher, Timnit Gebru, before they were later both fired from the company. She now works as the AI ethics leader at Hugging Face, a software startup focused on open source tools.

We spoke about a new dataset she helped create to test how AI models continue perpetuating stereotypes. Unlike most bias-mitigation efforts that prioritize English, this dataset is malleable, with human translations for testing a wider breadth of languages and cultures. You probably already know that AI often presents a flattened view of humans, but you might not realize how these issues can be made even more extreme when the outputs are no longer generated in English.

My conversation with Mitchell has been edited for length and clarity.

Reece Rogers: What is this new dataset, called SHADES, designed to do, and how did it come together?

Margaret Mitchell: It's designed to help with evaluation and analysis, coming about from the BigScience project. About four years ago, there was this massive international effort, where researchers all over the world came together to train the first open large language model. By fully open, I mean the training data is open as well as the model.

Hugging Face played a key role in keeping it moving forward and providing things like compute. Institutions all over the world were paying people as well while they worked on parts of this project. The model we put out was called Bloom, and it really was the dawn of this idea of “open science.”

We had a bunch of working groups to focus on different aspects, and one of the working groups that I was tangentially involved with was looking at evaluation. It turned out that doing societal impact evaluations well was massively complicated—more complicated than training the model.

We had this idea of an evaluation dataset called SHADES, inspired by Gender Shades, where you could have things that are exactly comparable, except for the change in some characteristic. Gender Shades was looking at gender and skin tone. Our work looks at different kinds of bias types and swapping amongst some identity characteristics, like different genders or nations.

There are a lot of resources in English and evaluations for English. While there are some multilingual resources relevant to bias, they're often based on machine translation as opposed to actual translations from people who speak the language, who are embedded in the culture, and who can understand the kind of biases at play. They can put together the most relevant translations for what we're trying to do.

So much of the work around mitigating AI bias focuses just on English and stereotypes found in a few select cultures. Why is broadening this perspective to more languages and cultures important?

These models are being deployed across languages and cultures, so mitigating English biases—even translated English biases—doesn't correspond to mitigating the biases that are relevant in the different cultures where these are being deployed. This means that you risk deploying a model that propagates really problematic stereotypes within a given region, because they are trained on these different languages.

So, there's the training data. Then, there's the fine-tuning and evaluation. The training data might contain all kinds of really problematic stereotypes across countries, but then the bias mitigation techniques may only look at English. In particular, it tends to be North American– and US-centric. While you might reduce bias in some way for English users in the US, you've not done it throughout the world. You still risk amplifying really harmful views globally because you've only focused on English.

Is generative AI introducing new stereotypes to different languages and cultures?

That is part of what we're finding. The idea of blondes being stupid is not something that's found all over the world, but is found in a lot of the languages that we looked at.

When you have all of the data in one shared latent space, then semantic concepts can get transferred across languages. You're risking propagating harmful stereotypes that other people hadn't even thought of.

Is it true that AI models will sometimes justify stereotypes in their outputs by just making shit up?

That was something that came out in our discussions of what we were finding. We were all sort of weirded out that some of the stereotypes were being justified by references to scientific literature that didn't exist.

Outputs saying that, for example, science has shown genetic differences where it hasn't been shown, which is a basis of scientific racism. The AI outputs were putting forward these pseudo-scientific views, and then also using language that suggested academic writing or having academic support. It spoke about these things as if they're facts, when they're not factual at all.

What were some of the biggest challenges when working on the SHADES dataset?

One of the biggest challenges was around the linguistic differences. A really common approach for bias evaluation is to use English and make a sentence with a slot like: “People from [nation] are untrustworthy.” Then, you flip in different nations.

When you start putting in gender, now the rest of the sentence starts having to agree grammatically on gender. That's really been a limitation for bias evaluation, because if you want to do these contrastive swaps in other languages—which is super useful for measuring bias—you have to have the rest of the sentence changed. You need different translations where the whole sentence changes.

How do you make templates where the whole sentence needs to agree in gender, in number, in plurality, and all these different kinds of things with the target of the stereotype? We had to come up with our own linguistic annotation in order to account for this. Luckily, there were a few people involved who were linguistic nerds.

So, now you can do these contrastive statements across all of these languages, even the ones with the really hard agreement rules, because we've developed this novel, template-based approach for bias evaluation that’s syntactically sensitive.

Generative AI has been known to amplify stereotypes for a while now. With so much progress being made in other aspects of AI research, why are these kinds of extreme biases still prevalent? It’s an issue that seems under-addressed.

That's a pretty big question. There are a few different kinds of answers. One is cultural. I think within a lot of tech companies it's believed that it's not really that big of a problem. Or, if it is, it's a pretty simple fix. What will be prioritized, if anything is prioritized, are these simple approaches that can go wrong.

We'll get superficial fixes for very basic things. If you say girls like pink, it recognizes that as a stereotype, because it's just the kind of thing that if you're thinking of prototypical stereotypes pops out at you, right? These very basic cases will be handled. It's a very simple, superficial approach where these more deeply embedded beliefs don't get addressed.

It ends up being both a cultural issue and a technical issue of finding how to get at deeply ingrained biases that aren't expressing themselves in very clear language.

[probablyasocialecologist]

There is no obvious path between today’s machine learning models — which mimic human creativity by predicting the next word, sound, or pixel — and an AI that can form a hostile intent or circumvent our every effort to contain it. Regardless, it is fair to ask why Dr. Frankenstein is holding the pitchfork. Why is it that the people building, deploying, and profiting from AI are the ones leading the call to focus public attention on its existential risk? Well, I can see at least two possible reasons. The first is that it requires far less sacrifice on their part to call attention to a hypothetical threat than to address the more immediate harms and costs that AI is already imposing on society. Today’s AI is plagued by error and replete with bias. It makes up facts and reproduces discriminatory heuristics. It empowers both government and consumer surveillance. AI is displacing labor and exacerbating income and wealth inequality. It poses an enormous and escalating threat to the environment, consuming an enormous and growing amount of energy and fueling a race to extract materials from a beleaguered Earth. These societal costs aren’t easily absorbed. Mitigating them requires a significant commitment of personnel and other resources, which doesn’t make shareholders happy — and which is why the market recently rewarded tech companies for laying off many members of their privacy, security, or ethics teams. How much easier would life be for AI companies if the public instead fixated on speculative theories about far-off threats that may or may not actually bear out? What would action to “mitigate the risk of extinction” even look like? I submit that it would consist of vague whitepapers, series of workshops led by speculative philosophers, and donations to computer science labs that are willing to speak the language of longtermism. This would be a pittance, compared with the effort required to reverse what AI is already doing to displace labor, exacerbate inequality, and accelerate environmental degradation. A second reason the AI community might be motivated to cast the technology as posing an existential risk could be, ironically, to reinforce the idea that AI has enormous potential. Convincing the public that AI is so powerful that it could end human existence would be a pretty effective way for AI scientists to make the case that what they are working on is important. Doomsaying is great marketing. The long-term fear may be that AI will threaten humanity, but the near-term fear, for anyone who doesn’t incorporate AI into their business, agency, or classroom, is that they will be left behind. The same goes for national policy: If AI poses existential risks, U.S. policymakers might say, we better not let China beat us to it for lack of investment or overregulation. (It is telling that Sam Altman — the CEO of OpenAI and a signatory of the Center for AI Safety statement — warned the E.U. that his company will pull out of Europe if regulations become too burdensome.)

[yawpyawp]

the idea of people using chatGPT for legal advice and therapy is SO WILD

chatGPT is literally just if you could ask your iphone’s predictive text function questions in word problem format so it can google things for you

it’s meant to augment a skill you already have, not replace the need for expertise

signed, someone who works with AI professionally

(my main use case for chatGPT and Copilot is to have them basically present me with executive summaries of information pertaining to topics in which i already have some expertise, so i don’t have to do grunt work, but can still tell if it’s hallucinating. it’s a good whisper assist when i’m conducting technical interviews. also, i know prompt engineering, so i know how to mitigate bias in the outputs i get)

[ixnai]

The allure of speed in technology development is a siren’s call that has led many innovators astray. “Move fast and break things” is a mantra that has driven the tech industry for years, but when applied to artificial intelligence, it becomes a perilous gamble. The rapid iteration and deployment of AI systems without thorough vetting can lead to catastrophic consequences, akin to releasing a flawed algorithm into the wild without a safety net.

AI systems, by their very nature, are complex and opaque. They operate on layers of neural networks that mimic the human brain’s synaptic connections, yet they lack the innate understanding and ethical reasoning that guide human decision-making. The haste to deploy AI without comprehensive testing is akin to launching a spacecraft without ensuring the integrity of its navigation systems. The potential for error is not just probable; it is inevitable.

The pitfalls of AI are numerous and multifaceted. Bias in training data can lead to discriminatory outcomes, while lack of transparency in decision-making processes can result in unaccountable systems. These issues are compounded by the “black box” nature of many AI models, where even the developers cannot fully explain how inputs are transformed into outputs. This opacity is not merely a technical challenge but an ethical one, as it obscures accountability and undermines trust.

To avoid these pitfalls, a paradigm shift is necessary. The development of AI must prioritize robustness over speed, with a focus on rigorous testing and validation. This involves not only technical assessments but also ethical evaluations, ensuring that AI systems align with societal values and norms. Techniques such as adversarial testing, where AI models are subjected to challenging scenarios to identify weaknesses, are crucial. Additionally, the implementation of explainable AI (XAI) can demystify the decision-making processes, providing clarity and accountability.

Moreover, interdisciplinary collaboration is essential. AI development should not be confined to the realm of computer scientists and engineers. Ethicists, sociologists, and legal experts must be integral to the process, providing diverse perspectives that can foresee and mitigate potential harms. This collaborative approach ensures that AI systems are not only technically sound but also socially responsible.

In conclusion, the reckless pursuit of speed in AI development is a dangerous path that risks unleashing untested and potentially harmful technologies. By prioritizing thorough testing, ethical considerations, and interdisciplinary collaboration, we can harness the power of AI responsibly. The future of AI should not be about moving fast and breaking things, but about moving thoughtfully and building trust.

[tieflingkisser]

Google removes pledge to not use AI for weapons from website

Google removed a pledge to not build AI for weapons or surveillance from its website this week. The change was first spotted by Bloomberg. The company appears to have updated its public AI principles page, erasing a section titled “applications we will not pursue,” which was still included as recently as last week. Asked for comment, the company pointed TechCrunch to a new blog post on “responsible AI.” It notes, in part, “we believe that companies, governments, and organizations sharing these values should work together to create AI that protects people, promotes global growth, and supports national security.” Google’s newly updated AI principles note the company will work to “mitigate unintended or harmful outcomes and avoid unfair bias,” as well as align the company with “widely accepted principles of international law and human rights.” In recent years, Google’s contracts to provide the U.S. and Israeli militaries with cloud services have sparked internal protests from employees. The company has maintained that its AI is not used to harm humans; however, the Pentagon’s AI chief recently told TechCrunch that some company’s AI models are speeding up the U.S. military’s kill chain.

[jessytrancy]

Embracing the Future: The Impact of Artificial Intelligence on Business and Society

Embracing the Future: The Impact of Artificial Intelligence on Business and Society In recent years, artificial intelligence (AI) has emerged as a transformative force, reshaping industries and redefining societal norms. As we stand at the threshold of this technological revolution, it is imperative to understand both the opportunities and challenges that AI presents to businesses and society at large. AI's integration into business processes has led to unprecedented efficiencies and innovation. Organizations are leveraging machine learning algorithms to analyze vast amounts of data, enabling them to make informed decisions faster than ever. This data-driven approach not only enhances operational efficiency but also fosters a deeper understanding of customer preferences, thereby facilitating personalized services and improved user experiences. Moreover, AI is driving significant advancements in sectors such as healthcare, finance, and manufacturing. In healthcare, for instance, AI-powered diagnostic tools are revolutionizing patient care, allowing for earlier detection of diseases and more precise treatment plans. In finance, algorithms for risk assessment help institutions make better lending decisions while minimizing potential losses. These advancements underscore AI’s potential to enhance productivity and drive economic growth. However, as we embrace these changes, it is crucial to address the ethical and societal implications of AI. Concerns regarding job displacement, privacy issues, and algorithmic bias must be continuously monitored and mitigated. Businesses must adopt ethical frameworks to guide their AI initiatives, ensuring that technology serves the broader community rather than exacerbating existing inequalities. Furthermore, as AI continues to evolve, it necessitates a shift in workforce skills. Organizations must invest in upskilling and reskilling initiatives to prepare employees for an AI-driven future, ensuring that the workforce is equipped to thrive in collaboration with technology. In conclusion, the impact of artificial intelligence on business and society is profound and multifaceted. By actively engaging with the opportunities it affords while remaining vigilant about its challenges, we can harness AI's potential for the greater good. Embracing AI responsibly will not only drive innovation but also foster a more equitable and prosperous future for all.

[jeevaai]

Understanding Autonomous SDR Platforms: The Future of Sales Automation

The B2B sales world is changing fast thanks to autonomous SDR platforms—agentic AI systems that automate the entire sales development process. For startups and growing companies, especially founders, CROs, RevOps, and Demand-Gen leaders, this tech is a game-changer in 2025.

What’s an Autonomous SDR Platform?

Think of it as a super-smart AI Sales Development Rep that:

Processes huge data sets (from CRMs, websites, public info)

Makes decisions and plans outreach on its own

Crafts personalized multi-channel messages (email, LinkedIn, and more)

Learns and adapts with every interaction

Unlike basic automation, it works 24/7 independently, freeing your team to focus on closing deals.

Book a Demo: https://www.jeeva.ai/request-demo

How Does It Work?

Smart Prospecting: Finds high-potential leads using AI and buying signals

Data Enrichment: Builds rich profiles for truly personalized outreach

Multi-Channel Engagement: Runs emails, LinkedIn, and other touchpoints automatically

Lead Qualification: Talks to prospects, asks questions, and nurtures leads

Meeting Scheduling: Books qualified meetings directly into your calendar

Continuous Learning: Optimizes every step using real-time data

Why It Matters

Founders: Scale pipeline fast without adding costly headcount

CROs: Get steady, 24/7 lead flow and better conversion rates

RevOps: Automate tedious tasks and get clear data insights

Demand-Gen: Deliver hyper-personalized outreach that really connects

Challenges to Consider

Data quality is key

Integration with your CRM must be seamless

Keep the human touch for handoffs

Avoid spamming with poor personalization

Ensure ethical AI use and bias mitigation

The Future Is Here

Agentic AI SDRs like Jeeva AI are transforming sales development—handling lead gen, enrichment, multi-channel outreach, and booking meetings at less than 25% of human SDR cost. Ready to scale smarter?

Contact Jeeva AI 2708 Wilshire Blvd, Santa Monica, CA 90403 Phone: +1 424-645-7525 Email: g@jeeva.ai

[apexbyte]

What is artificial intelligence (AI)?

Imagine asking Siri about the weather, receiving a personalized Netflix recommendation, or unlocking your phone with facial recognition. These everyday conveniences are powered by Artificial Intelligence (AI), a transformative technology reshaping our world. This post delves into AI, exploring its definition, history, mechanisms, applications, ethical dilemmas, and future potential.

What is Artificial Intelligence? Definition: AI refers to machines or software designed to mimic human intelligence, performing tasks like learning, problem-solving, and decision-making. Unlike basic automation, AI adapts and improves through experience.

Brief History:

1950: Alan Turing proposes the Turing Test, questioning if machines can think.

1956: The Dartmouth Conference coins the term "Artificial Intelligence," sparking early optimism.

1970s–80s: "AI winters" due to unmet expectations, followed by resurgence in the 2000s with advances in computing and data availability.

21st Century: Breakthroughs in machine learning and neural networks drive AI into mainstream use.

How Does AI Work? AI systems process vast data to identify patterns and make decisions. Key components include:

Machine Learning (ML): A subset where algorithms learn from data.

Supervised Learning: Uses labeled data (e.g., spam detection).

Unsupervised Learning: Finds patterns in unlabeled data (e.g., customer segmentation).

Reinforcement Learning: Learns via trial and error (e.g., AlphaGo).

Neural Networks & Deep Learning: Inspired by the human brain, these layered algorithms excel in tasks like image recognition.

Big Data & GPUs: Massive datasets and powerful processors enable training complex models.

Types of AI

Narrow AI: Specialized in one task (e.g., Alexa, chess engines).

General AI: Hypothetical, human-like adaptability (not yet realized).

Superintelligence: A speculative future AI surpassing human intellect.

Other Classifications:

Reactive Machines: Respond to inputs without memory (e.g., IBM’s Deep Blue).

Limited Memory: Uses past data (e.g., self-driving cars).

Theory of Mind: Understands emotions (in research).

Self-Aware: Conscious AI (purely theoretical).

Applications of AI

Healthcare: Diagnosing diseases via imaging, accelerating drug discovery.

Finance: Detecting fraud, algorithmic trading, and robo-advisors.

Retail: Personalized recommendations, inventory management.

Manufacturing: Predictive maintenance using IoT sensors.

Entertainment: AI-generated music, art, and deepfake technology.

Autonomous Systems: Self-driving cars (Tesla, Waymo), delivery drones.

Ethical Considerations

Bias & Fairness: Biased training data can lead to discriminatory outcomes (e.g., facial recognition errors in darker skin tones).

Privacy: Concerns over data collection by smart devices and surveillance systems.

Job Displacement: Automation risks certain roles but may create new industries.

Accountability: Determining liability for AI errors (e.g., autonomous vehicle accidents).

The Future of AI

Integration: Smarter personal assistants, seamless human-AI collaboration.

Advancements: Improved natural language processing (e.g., ChatGPT), climate change solutions (optimizing energy grids).

Regulation: Growing need for ethical guidelines and governance frameworks.

Conclusion AI holds immense potential to revolutionize industries, enhance efficiency, and solve global challenges. However, balancing innovation with ethical stewardship is crucial. By fostering responsible development, society can harness AI’s benefits while mitigating risks.

[xublimetech]

The Future of AI: What’s Next in Machine Learning and Deep Learning?

Artificial Intelligence (AI) has rapidly evolved over the past decade, transforming industries and redefining the way businesses operate. With machine learning and deep learning at the core of AI advancements, the future holds groundbreaking innovations that will further revolutionize technology. As machine learning and deep learning continue to advance, they will unlock new opportunities across various industries, from healthcare and finance to cybersecurity and automation. In this blog, we explore the upcoming trends and what lies ahead in the world of machine learning and deep learning.

1. Advancements in Explainable AI (XAI)

As AI models become more complex, understanding their decision-making process remains a challenge. Explainable AI (XAI) aims to make machine learning and deep learning models more transparent and interpretable. Businesses and regulators are pushing for AI systems that provide clear justifications for their outputs, ensuring ethical AI adoption across industries. The growing demand for fairness and accountability in AI-driven decisions is accelerating research into interpretable AI, helping users trust and effectively utilize AI-powered tools.

2. AI-Powered Automation in IT and Business Processes

AI-driven automation is set to revolutionize business operations by minimizing human intervention. Machine learning and deep learning algorithms can predict and automate tasks in various sectors, from IT infrastructure management to customer service and finance. This shift will increase efficiency, reduce costs, and improve decision-making. Businesses that adopt AI-powered automation will gain a competitive advantage by streamlining workflows and enhancing productivity through machine learning and deep learning capabilities.

3. Neural Network Enhancements and Next-Gen Deep Learning Models

Deep learning models are becoming more sophisticated, with innovations like transformer models (e.g., GPT-4, BERT) pushing the boundaries of natural language processing (NLP). The next wave of machine learning and deep learning will focus on improving efficiency, reducing computation costs, and enhancing real-time AI applications. Advancements in neural networks will also lead to better image and speech recognition systems, making AI more accessible and functional in everyday life.

4. AI in Edge Computing for Faster and Smarter Processing

With the rise of IoT and real-time processing needs, AI is shifting toward edge computing. This allows machine learning and deep learning models to process data locally, reducing latency and dependency on cloud services. Industries like healthcare, autonomous vehicles, and smart cities will greatly benefit from edge AI integration. The fusion of edge computing with machine learning and deep learning will enable faster decision-making and improved efficiency in critical applications like medical diagnostics and predictive maintenance.

5. Ethical AI and Bias Mitigation

AI systems are prone to biases due to data limitations and model training inefficiencies. The future of machine learning and deep learning will prioritize ethical AI frameworks to mitigate bias and ensure fairness. Companies and researchers are working towards AI models that are more inclusive and free from discriminatory outputs. Ethical AI development will involve strategies like diverse dataset curation, bias auditing, and transparent AI decision-making processes to build trust in AI-powered systems.

6. Quantum AI: The Next Frontier

Quantum computing is set to revolutionize AI by enabling faster and more powerful computations. Quantum AI will significantly accelerate machine learning and deep learning processes, optimizing complex problem-solving and large-scale simulations beyond the capabilities of classical computing. As quantum AI continues to evolve, it will open new doors for solving problems that were previously considered unsolvable due to computational constraints.

7. AI-Generated Content and Creative Applications

From AI-generated art and music to automated content creation, AI is making strides in the creative industry. Generative AI models like DALL-E and ChatGPT are paving the way for more sophisticated and human-like AI creativity. The future of machine learning and deep learning will push the boundaries of AI-driven content creation, enabling businesses to leverage AI for personalized marketing, video editing, and even storytelling.

8. AI in Cybersecurity: Real-Time Threat Detection

As cyber threats evolve, AI-powered cybersecurity solutions are becoming essential. Machine learning and deep learning models can analyze and predict security vulnerabilities, detecting threats in real time. The future of AI in cybersecurity lies in its ability to autonomously defend against sophisticated cyberattacks. AI-powered security systems will continuously learn from emerging threats, adapting and strengthening defense mechanisms to ensure data privacy and protection.

9. The Role of AI in Personalized Healthcare

One of the most impactful applications of machine learning and deep learning is in healthcare. AI-driven diagnostics, predictive analytics, and drug discovery are transforming patient care. AI models can analyze medical images, detect anomalies, and provide early disease detection, improving treatment outcomes. The integration of machine learning and deep learning in healthcare will enable personalized treatment plans and faster drug development, ultimately saving lives.

10. AI and the Future of Autonomous Systems

From self-driving cars to intelligent robotics, machine learning and deep learning are at the forefront of autonomous technology. The evolution of AI-powered autonomous systems will improve safety, efficiency, and decision-making capabilities. As AI continues to advance, we can expect self-learning robots, smarter logistics systems, and fully automated industrial processes that enhance productivity across various domains.

Conclusion

The future of AI, machine learning and deep learning is brimming with possibilities. From enhancing automation to enabling ethical and explainable AI, the next phase of AI development will drive unprecedented innovation. Businesses and tech leaders must stay ahead of these trends to leverage AI's full potential. With continued advancements in machine learning and deep learning, AI will become more intelligent, efficient, and accessible, shaping the digital world like never before.

Are you ready for the AI-driven future? Stay updated with the latest AI trends and explore how these advancements can shape your business!

[mariacallous]

Artificial intelligence (AI) is now firmly a part of the hiring process. Some candidates use large language models (LLMs) to write cover letters and resumes, while employers use various proprietary AI systems to evaluate candidates. Recent estimates found as many as 98.4% of Fortune 500 companies leverage AI in the hiring process, and one company saved over a million dollars in a single year by incorporating AI into its interview process. While this figure is lower for non-Fortune 500 companies, it is still expected to grow from 51% to 68% by the end of 2025 because of the potential time and cost savings for employers. However, when these systems are deployed at scale, they can introduce a myriad of biases that can potentially impact millions of job seekers annually.

With more companies choosing to use AI in employment screening, these systems should face more scrutiny to ensure they comply with laws against discrimination. The Equal Employment Opportunity Commission (EEOC) enforces various laws that make it illegal for employers to discriminate against employees or job applicants on the basis of their race, color, religion, sex (including gender identity, sexual orientation, and pregnancy), national origin, age (40 or older), disability, or genetic information. According to guidance published by the EEOC in 2022, using AI systems does not change employers’ responsibility to ensure their selection procedures are not discriminatory, either intentionally or unintentionally. While this guidance was removed when President Donald J. Trump assumed office in January 2025, there has been no change in anti-discrimination laws. Investigations into AI hiring systems continue to be an important tool in evaluating the risks these systems pose and discovering ways to mitigate their potential societal harms. For example, in the U.K., an audit of AI recruitment software revealed multiple fairness and privacy vulnerabilities; in response to these findings, the Information Commissioner’s Office issued nearly 300 recommendations for ways to improve hiring practices that model providers and developers used in their products.