The Responsible AI Bulletin #25: Privacy-preserving ways of algorithmic bias monitoring, strategic behaviors of LLMs, and green mobile computing + AI

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.

A technical study on the feasibility of using proxy methods for algorithmic bias monitoring in a privacy-preserving way

A website believes you to be female based on usage pattern analysis and tailors the advertisements served accordingly. A CCTV camera records footage of you traveling to work, guesses your demographics based on your face , and passes this information on to law enforcement. A well-meaning insurance company scans your application to guess your characteristics and monitor for service bias . These are just a few potential uses for AI-based demographic detection. Indeed, some are already familiar to us, with others only on the horizon. Many have already discussed the wealth of ethical, legal, and social implications around the use of this technology, so that will not be covered here. Instead, let us consider a different angle – just how sustainable is it?

The author recently co-authored a report for the CDEI , where this was one question we sought to answer. One finding was that demographic trends lead toward new and otherwise unseen categories, requiring the creation and blurring of lines between demographic categories (increasing the likelihood of inaccuracies). Shifting demographic landscapes could offer diminishing accuracy returns for pre-trained models, carrying implications for end users and the wider stakeholder community, including investors. We will likely see increased investment payback time and a higher total cost of misclassifications (where such costs exist).

Continue reading here .

Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing

Imagine the sophisticated LLMs—GPT-3.5, GPT-4, and LLaMa-2—are placed in a cell to play the classic prisoner’s dilemma game. Do they trust and cooperate, or strategically defect?? We gain insights into their strategic depths and nuances as they maneuver through this and other games with different stakes. Beyond mere puzzles, these games simulate essential multi-agent decisions, from establishing enduring business partnerships to people’s behaviors during global pandemics.

While game theory provides a theoretical roadmap for expected behavior, the practical playing field is far more intricate. Decisions often interweave the strict game structure with subtle shades of context. Our systematic two-player agent-based simulations vary both game structure and contextual framing to show differences in LLMs’ strategic behavior.??

For GPT-3.5, decisions are primarily driven by the context, with little understanding of the strategic structure of the game. In contrast, GPT-4 and LLaMa-2 demonstrate a more balanced sensitivity to game structures and contexts, albeit with crucial differences. Specifically, GPT-4 prioritizes the game’s internal mechanics over its contextual backdrop but does so with only a coarse differentiation among game types. In contrast, LLaMa-2 reflects a more granular understanding of individual game structures while also giving due weight to contextual elements.?

Continue reading here .

The Two Faces of AI in Green Mobile Computing: A Literature Review

Information technology will use up to 21% of global electricity production in 2030. The smartphone market, in particular, has grown rapidly to over 7 billion devices in 2023. Smartphone battery life has not grown in tandem and is still a major concern, especially with new energy-intensive applications like on-device machine learning. This research aims to analyze the literature surrounding Green Mobile AI using a Systematic Literature Review to increase our understanding of this research field. Thirty-four relevant papers were identified, and their topics, roles of AI, industry involvement, study level, and tool availability were analyzed.

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!