Games for Fairness and Interpretability

• URL: http://arxiv.org/abs/2004.09551v1
• Date: Mon, 20 Apr 2020 18:09:32 GMT
• Title: Games for Fairness and Interpretability
• Authors: Eric Chu, Nabeel Gillani, Sneha Priscilla Makini
• Abstract summary: We propose a new class of games -- games for fairness and interpretability'' Games for fairness and interpretability are carefully-designed games with mass appeal. We highlight several possible examples of games, their implications for fairness and interpretability, and why the machine learning community could benefit from them.
• Score: 2.084078990567849
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As Machine Learning (ML) systems becomes more ubiquitous, ensuring the fair and equitable application of their underlying algorithms is of paramount importance. We argue that one way to achieve this is to proactively cultivate public pressure for ML developers to design and develop fairer algorithms -- and that one way to cultivate public pressure while simultaneously serving the interests and objectives of algorithm developers is through gameplay. We propose a new class of games -- ``games for fairness and interpretability'' -- as one example of an incentive-aligned approach for producing fairer and more equitable algorithms. Games for fairness and interpretability are carefully-designed games with mass appeal. They are inherently engaging, provide insights into how machine learning models work, and ultimately produce data that helps researchers and developers improve their algorithms. We highlight several possible examples of games, their implications for fairness and interpretability, how their proliferation could creative positive public pressure by narrowing the gap between algorithm developers and the general public, and why the machine learning community could benefit from them.

Related papers

• Mechanic Maker: Accessible Game Development Via Symbolic Learning Program Synthesis [2.3941497253612085]
  Mechanic Maker is a tool for creating a wide-range of game mechanics without programming. It relies on a backend symbolic learning system to synthesize game mechanics from examples.
  arXiv  Detail & Related papers  (2024-10-01T21:58:28Z)
• Neural Population Learning beyond Symmetric Zero-sum Games [52.20454809055356]
  We introduce NeuPL-JPSRO, a neural population learning algorithm that benefits from transfer learning of skills and converges to a Coarse Correlated (CCE) of the game. Our work shows that equilibrium convergent population learning can be implemented at scale and in generality.
  arXiv  Detail & Related papers  (2024-01-10T12:56:24Z)
• Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games [58.720142291102135]
  We describe an effort to add an experimental reinforcement learning system to an existing automated game testing solution based on scripted bots. We show a use-case of leveraging reinforcement learning in game production and cover some of the largest time sinks anyone who wants to make the same journey for their game may encounter. We propose a few research directions that we believe will be valuable and necessary for making machine learning, and especially reinforcement learning, an effective tool in game production.
  arXiv  Detail & Related papers  (2023-07-19T18:19:23Z)
• Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness? [8.679212948810916]
  Several fairness pre-processing algorithms are available to alleviate implicit biases during model training. These algorithms employ different concepts of fairness, often leading to conflicting strategies with consequential trade-offs between fairness and accuracy. We evaluate three popular fairness pre-processing algorithms and investigate the potential for combining all algorithms into a more robust pre-processing ensemble.
  arXiv  Detail & Related papers  (2022-12-05T21:54:29Z)
• Modeling Content Creator Incentives on Algorithm-Curated Platforms [76.53541575455978]
  We study how algorithmic choices affect the existence and character of (Nash) equilibria in exposure games. We propose tools for numerically finding equilibria in exposure games, and illustrate results of an audit on the MovieLens and LastFM datasets.
  arXiv  Detail & Related papers  (2022-06-27T08:16:59Z)
• Discovering Multi-Agent Auto-Curricula in Two-Player Zero-Sum Games [31.97631243571394]
  We introduce a framework, LMAC, that automates the discovery of the update rule without explicit human design. Surprisingly, even without human design, the discovered MARL algorithms achieve competitive or even better performance. We show that LMAC is able to generalise from small games to large games, for example training on Kuhn Poker and outperforming PSRO.
  arXiv  Detail & Related papers  (2021-06-04T22:30:25Z)
• An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games [79.23847247132345]
  This work investigates how well an artificial agent can benefit from playing guessing games when later asked to perform on novel NLP downstream tasks such as Visual Question Answering (VQA) We propose two ways to exploit playing guessing games: 1) a supervised learning scenario in which the agent learns to mimic successful guessing games and 2) a novel way for an agent to play by itself, called Self-play via Iterated Experience Learning (SPIEL)
  arXiv  Detail & Related papers  (2021-01-31T10:30:48Z)
• Learning outside the Black-Box: The pursuit of interpretable models [78.32475359554395]
  This paper proposes an algorithm that produces a continuous global interpretation of any given continuous black-box function. Our interpretation represents a leap forward from the previous state of the art.
  arXiv  Detail & Related papers  (2020-11-17T12:39:44Z)
• Algorithms in Multi-Agent Systems: A Holistic Perspective from Reinforcement Learning and Game Theory [2.5147566619221515]
  Deep reinforcement learning has achieved outstanding results in recent years. Recent works are exploring learning beyond single-agent scenarios and considering multi-agent scenarios. Traditional game-theoretic algorithms, which, in turn, show bright application promise combined with modern algorithms and boosting computing power.
  arXiv  Detail & Related papers  (2020-01-17T15:08:04Z)
• Smooth markets: A basic mechanism for organizing gradient-based learners [47.34060971879986]
  We introduce smooth markets (SM-games), a class of n-player games with pairwise zero sum interactions. SM-games codify a common design pattern in machine learning that includes (some) GANs, adversarial training, and other recent algorithms. We show that SM-games are amenable to analysis and optimization using first-order methods.
  arXiv  Detail & Related papers  (2020-01-14T09:19:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.