Bias Archives - Liwaiwai

Machine Learning

Large Language Models Are Biased. Can Logic Help Save Them?

Turns out, even language models “think” they’re biased. When prompted in ChatGPT, the response was as follows: “Yes, language models can have biases, because the training data reflects the biases present in society from which that data was collected. For example, gender and racial biases are prevalent in many real-world datasets, and if a language model is trained on that, it can perpetuate and amplify these biases in its predictions.” A well-known but dangerous problem. Humans (typically) can dabble with both logical and stereotypical reasoning when learning. Still, language models mainly mimic the latter, an unfortunate narrative we’ve seen play…

Artificial Intelligence

Subtle Biases In AI Can Influence Emergency Decisions

It’s no secret that people harbor biases — some unconscious, perhaps, and others painfully overt. The average person might suppose that computers — machines typically made of plastic, steel, glass, silicon, and various metals — are free of prejudice. While that assumption may hold for computer hardware, the same is not always true for computer software, which is programmed by fallible humans and can be fed data that is, itself, compromised in certain respects. Artificial intelligence (AI) systems — those based on machine learning, in particular — are seeing increased use in medicine for diagnosing specific diseases, for example, or…

IBM, Delta Air Lines, WPP, 4A’s, IAB, Ad Council And Other Industry Leaders Act To Mitigate Bias In Advertising Technology

Today, leading companies committed to improving fairness in marketing campaigns. The initiative, announced at the Cannes Lions International Festival of Creativity 2022, brought together agencies, brands, and other leaders to generate awareness and take action towards mitigating bias in advertising technology. Committing organizations include IBM (NYSE: IBM), Delta Air Lines, WPP, Mindshare, 4A’s, IAB and the Ad Council. The action is the most recent effort by IBM to drive education and awareness around the impact of bias in advertising technology. In 2021, the company launched a research initiative to explore the hypothesis that bias can exist in ad technology, which initial findings confirmed. The research also showed…

Artificial Intelligence

Their Stories Should Be Celebrated: Using AI To Deliver More Inclusive Biographical Content On Wikipedia

Wikipedia, which is consistently ranked one of the top 10 most visited websites, is often the first stop for many people looking for information about historical figures and changemakers. But not everyone is equally represented on Wikipedia. Only about 20 percent of biographies on the English site are about women, according to the Wikimedia Foundation, and we imagine that percentage is even smaller for women from intersectional groups, such as women in science, women in Africa, and women in Asia. For my PhD project as a computer science student at the Université de Lorraine, CNRS, in France, I worked with my adviser, Claire…

Can Machine-Learning Models Overcome Biased Datasets?

Artificial intelligence systems may be able to complete tasks quickly, but that doesn’t mean they always do so fairly. If the datasets used to train machine-learning models contain biased data, it is likely the system could exhibit that same bias when it makes decisions in practice. For instance, if a dataset contains mostly images of white men, then a facial-recognition model trained with these data may be less accurate for women or people with different skin tones. A group of researchers at MIT, in collaboration with researchers at Harvard University and Fujitsu Ltd., sought to understand when and how a…

The Man, The Machine, And The Black Box: ML Observability

In this talk, Aparna Dhinakaran, Co-Founder and CPO of Arize AI, covered the challenges organizations face in checking for model fairness, such as the lack of access to protected class information to check for bias and diffuse organizational responsibility of ensuring model fairness. Aparna also dived into the approaches organizations can take to start addressing ML fairness head-on with a technical overview of fairness definitions and how practical tools such as ML Observability can help build ML fairness checks into the ML workflow. If you’ve heard of Michelangelo, Aparna built part of the model store, which was eventually kind of integrated into…

IBM Researchers Check AI Bias With Counterfactual Text

From recruitment to credit risk apps, or use in the healthcare and criminal justice system, AI unreliability has been giving researchers headaches for years. We’ve tackled the issue by flipping it upside down. Our team has developed an AI that verifies other AIs’ “fairness” by generating a set of counterfactual text samples and testing machine learning systems without supervision. In our recent paper, “Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text” accepted to AAAI 2021, we describe how our software, dubbed GYC, generates test cases to check the reliability of AI models. GYC can evaluate other AIs for accuracy,…

Data

Here Are 13 Subtle Ways Statistics Can Deceive You

No one can argue with statistics because they are hard facts. — are they really? By this time, a lot of us have done a great job realising that not everything we see on the internet is true. Some of the information we receive is deliberately manipulated in order to influence our behavior or our perceptions With this, we become highly skeptical. We look for reputable sources. We turn to the hard facts. For instance, figures and statistics, right? No. Statistics is a tool used to navigate in this world filled with uncertainty and risk. The figures it produces are…

Algorithms Trace How Stereotypes Have Changed

Word embeddings—an algorithmic technique that can map relationships and associations between words—can measure changes in gender and ethnic stereotypes over the past century in the United States. Researchers analyzed large databases of American books, newspapers, and other texts and looked at how those linguistic changes correlated with actual US Census demographic data and major social shifts such as the women’s movement in the 1960s and the increase in Asian immigration, according to the research. Artificial intelligence systems and machine-learning algorithms have come under fire recently because they can pick up and reinforce existing biases in our society, depending on what…

To Make Sense Of AI Decisions, ‘Peek Under The Hood’

Now that humans have programmed computers to learn, we want to know exactly what they’ve learned and how they make decisions after their learning process is complete. The answers to such questions could shed light on our own decision-making processes. Kate Saenko, an associate professor of computer science at Boston University, asked humans to look at dozens of pictures depicting steps that the computer may have taken on its road to a decision, and identify its most likely path. The humans gave answers that made sense, but there was a problem: they made sense to humans, and humans, Saenko knew,…

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