The Responsible AI Bulletin #24: Collectionless AI, HAI+societal pitfalls, and prediction-modelers and decision-makers.

Welcome to this edition of The Responsible AI Bulletin, a weekly agglomeration of research developments in the field from around the Internet that caught my attention - a few morsels to dazzle in your next discussion on AI, its ethical implications, and what it means for our future.

For those looking for more detailed investigations into research and reporting in the field of Responsible AI, I recommend subscribing to the AI Ethics Brief, published by my team at the Montreal AI Ethics Institute, an international non-profit research institute with a mission to democratize AI ethics literacy.

Collectionless Artificial Intelligence

The outstanding results of machine learning-based applications are largely due to models that are trained on huge datasets. This triggers several questions about the nature of such datasets and the way they are exploited:

• What’s inside these data collections, and who owns them? 

• Who has the resources for developing agents that learn from these huge collections?

An artificial agent learning from a large dataset inherits biases and gains skills that are directly related to the collection’s contents. Moreover, data means “power” since owning large collections allows training large models that are then exploited in downstream applications if and only if someone has access to significant hardware and energy resources.

“Collectionless AI” identifies those approaches where intelligent agents do not need to accumulate sensory data, processing samples without storing them when they are acquired from the environment. Environmental interactions, including the information coming from humans, play a crucial role in the learning process and offer control, as well as agent-by-agent communication. We think of agents that edge computing devices can manage, and this requires thinking of new learning protocols where machines learn in a lifelong manner. 

Continue reading here.

Human-AI Interactions and Societal Pitfalls

Generative artificial intelligence (AI) systems have improved at a rapid pace. For example, ChatGPT recently showcased its advanced capacity to perform complex tasks and human-like behaviors. However, have you noticed that content generated with the help of AI may not be the same as content generated without AI? In particular, the boost in productivity may come at the expense of users’ idiosyncrasies, such as personal style and tastes, preferences we would naturally express without AI. To better align our intentions with AI’s outputs (i.e., output fidelity), we have to spend more time and effort (i.e., communication cost) to edit our prompts or revise the AI-generated output ourselves. But what is the impact of this tradeoff at the individual and aggregate levels?

To study this effect, we propose a Bayesian framework in which rational users decide how much information to share with the AI, facing a trade-off between output fidelity and communication cost. We show that the interplay between these individual-level decisions and AI training may lead to societal challenges. Outputs may become more homogenized, especially when the AI is trained on AI-generated content. And any AI bias may become societal bias. A solution to the homogenization and bias issues is facilitating human-AI interactions, enabling personalized outputs without sacrificing productivity. 

Continue reading here.

On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model

Many of today’s algorithmic decision systems rely on machine learning predictions. In the ML language, a prediction does not necessarily refer to a future event (e.g., whether a customer will return to my e-commerce store). It simply refers to an unknown fact at the time of a decision (e.g., whether a credit applicant is trustworthy). So, one may ask: What is the role of prediction in modern algorithmic decision-making? How does it impact the fairness of a decision? The authors argue that distinguishing between prediction and decision is key to tackling algorithmic unfairness. While fairness is often linked to the features of a prediction model, the authors contend that what is truly “fair” or “unfair” pertains to the broader decision system rather than the prediction model. As they put it, “Fairness is about the real-world consequences on human lives resulting from a decision, not merely a prediction.” This motivated a  framework that distinguishes between the role of the ‘prediction-modeler’ and the role of the ‘decision-maker’ and specifies the information required from each for implementing fairness in a prediction-aided decision system. Based on a theoretical analysis of the interactions between the ‘prediction-modeler’ and the ‘decision-maker,’ the authors identify the information gaps that must be addressed to implement fairness measures in the decision system.

Continue reading here.

Comment and let me know what you liked and if you have any recommendations on what I should read and cover next week. You can learn more about my work here. See you soon!