The world is awash with news of AI and its potential to revolutionise decision-making but my post yesterday led to an interesting debate about how far a board should go to embrace the technology and utilise it in decision making? Floris Mertens, a legal scholar from the Financial Law Institute, has suggested that it may even evolve into a duty for boards to rely on AI in their decision-making processes. This comes at a time when AI has been making headlines almost every day as more and more people wake up to the evolution of generative pre trained AI. Use of AI in various fields has already shown hugely impressive results. Yesterday, law firm Allen & Overy announced its use of AI in writing contracts and correspondence to clients, Corsearch have been using it for a while in the niche of Trademark law and in one of my areas of deep interest, film, we're experiencing the rise of Deep Fakes and AI software to help with automatic dialogue replacement and to develop scripts and lyrics!

The Federal Trade Commission is opening a dedicated technology office that will place Silicon Valley under more scrutiny and help it stay on top of emerging tech and trends in a fast-moving market. Commissioners voted 4-0 on Thursday to create the office. Under the direction of chair Lina Khan, the FTC has trained its focus on tech companies. Last year, Epic Games agreed to a record $520 million settlement following FTC allegations that it violated the Children's Online Privacy Protection Act. The agency has also attempted to block Microsoft's proposed takeover of Activision Blizzard and sued to stop NVIDIA from buying ARM (NVIDIA backed out of the deal).

Existing research on human-AI collaborative decision-making focuses mainly on the interaction between AI and individual decision-makers. There is a limited understanding of how AI may perform in group decision-making. This paper presents a wizard-of-oz study in which two participants and an AI form a committee to rank three English essays. One novelty of our study is that we adopt a speculative design by endowing AI equal power to humans in group decision-making.We enable the AI to discuss and vote equally with other human members. We find that although the voice of AI is considered valuable, AI still plays a secondary role in the group because it cannot fully follow the dynamics of the discussion and make progressive contributions. Moreover, the divergent opinions of our participants regarding an "equal AI" shed light on the possible future of human-AI relations.

Collecting personally identifiable information (PII) on data subjects has become big business. Data brokers and data processors are part of a multi-billion-dollar industry that profits from collecting, buying, and selling consumer data. Yet there is little transparency in the data collection industry which makes it difficult to understand what types of data are being collected, used, and sold, and thus the risk to individual data subjects. In this study, we examine a large textual dataset of privacy policies from 1997-2019 in order to investigate the data collection activities of data brokers and data processors. We also develop an original lexicon of PII-related terms representing PII data types curated from legislative texts. This mesoscale analysis looks at privacy policies overtime on the word, topic, and network levels to understand the stability, complexity, and sensitivity of privacy policies over time. We find that (1) privacy legislation correlates with changes in stability and turbulence of PII data types in privacy policies; (2) the complexity of privacy policies decreases over time and becomes more regularized; (3) sensitivity rises over time and shows spikes that are correlated with events when new privacy legislation is introduced.

To mitigate the effects of undesired biases in models, several approaches propose to pre-process the input dataset to reduce the risks of discrimination by preventing the inference of sensitive attributes. Unfortunately, most of these pre-processing methods lead to the generation a new distribution that is very different from the original one, thus often leading to unrealistic data. As a side effect, this new data distribution implies that existing models need to be re-trained to be able to make accurate predictions. To address this issue, we propose a novel pre-processing method, that we coin as fair mapping, based on the transformation of the distribution of protected groups onto a chosen target one, with additional privacy constraints whose objective is to prevent the inference of sensitive attributes. More precisely, we leverage on the recent works of the Wasserstein GAN and AttGAN frameworks to achieve the optimal transport of data points coupled with a discriminator enforcing the protection against attribute inference. Our proposed approach, preserves the interpretability of data and can be used without defining exactly the sensitive groups. In addition, our approach can be specialized to model existing state-of-the-art approaches, thus proposing a unifying view on these methods. Finally, several experiments on real and synthetic datasets demonstrate that our approach is able to hide the sensitive attributes, while limiting the distortion of the data and improving the fairness on subsequent data analysis tasks.

Predictive algorithms are now used to help distribute a large share of our society's resources and sanctions, such as healthcare, loans, criminal detentions, and tax audits. Under the right circumstances, these algorithms can improve the efficiency and equity of decision-making. At the same time, there is a danger that the algorithms themselves could entrench and exacerbate disparities, particularly along racial, ethnic, and gender lines. To help ensure their fairness, many researchers suggest that algorithms be subject to at least one of three constraints: (1) no use of legally protected features, such as race, ethnicity, and gender; (2) equal rates of "positive" decisions across groups; and (3) equal error rates across groups. Here we show that these constraints, while intuitively appealing, often worsen outcomes for individuals in marginalized groups, and can even leave all groups worse off. The inherent trade-off we identify between formal fairness constraints and welfare improvements -- particularly for the marginalized -- highlights the need for a more robust discussion on what it means for an algorithm to be "fair". We illustrate these ideas with examples from healthcare and the criminal-legal system, and make several proposals to help practitioners design more equitable algorithms.

"Jidoka" is a new one to me. TRI (Toyota Research Institute) CEO Gill Pratt described the concept as "Automation with a Human Touch." The anglicized version of the notion is "Autonomation" -- both are modified forms of " automation," in their respective languages. The word was originally applied to Toyota's Production System, highlighting the need for human participation in the process. Employing Jidoka principles throughout the production process is a vital element of the Toyota Production System, forcing imperfections to be immediately addressed by self-inspecting workers and thereby reducing the amount of work added to a defective product.

As the fervor around generative AI grows, critics have called on the creators of the tech to take steps to mitigate its potentially harmful effects. In particular, text-generating AI in particular has gotten a lot of attention -- and with good reason. Students could use it to plagiarize, content farms could use it to spam and bad actors could use it to spread misinformation. OpenAI bowed to pressure several weeks ago, releasing a classifier tool that attempts to distinguish between human-written and synthetic text. But it's not particularly accurate; OpenAI estimates that it misses 74% of AI-generated text. In the absence of a reliable way to spot text originating from an AI, a cottage industry of detector services has sprung up.

The system for granting asylum in the U.S. has long been a political point of contention. Democrats and Republicans debate how liberal or restrictive its rules should be, but evidence suggests that the fate of some asylum seekers may be less influenced by the rules than by something far more arbitrary: the judge they're assigned. A 2007 study titled "Refugee Roulette" found that one judge granted asylum to only 5 percent of Colombian applicants, whereas another--working in the same building and applying the same rules--granted it to 88 percent. Asylum is by no means the only part of our legal system where such discrepancies arise. In a landmark 1974 study, 50 judges were given an identical set of facts about a hypothetical heroin dealer.

In this thorough study, we took a closer look at the skepticism that has arisen with respect to potential dangers associated with artificial intelligence - denoted as AI Risk Skepticism. Our study takes into account different points of view on the topic and draws parallels with other forms of skepticism that have shown up in science. We categorize the various skepticisms regarding the dangers of AI by the type of mistaken thinking involved. We hope this will be of interest and value to AI researchers concerned about the future of AI and the risks that it may pose. The issues of skepticism and risk in AI are decidedly important and require serious consideration. By addressing these issues with the rigor and precision of scientific research, we hope to better understand the objections we face and to find satisfactory ways to resolve them.