Many safety failures in machine learning arise when models are used to assign predictions to people (often in settings like lending, hiring, or content moderation) without accounting for how individuals can change their inputs. In this work, we introduce a formal validation procedure for the responsiveness of predictions with respect to interventions on their features. Our procedure frames responsiveness as a type of sensitivity analysis in which practitioners control a set of changes by specifying constraints over interventions and distributions over downstream effects. We describe how to estimate responsiveness for the predictions of any model and any dataset using only black-box access, and how to use these estimates to support tasks such as falsification and failure probability estimation. We develop algorithms that construct these estimates by generating a uniform sample of reachable points, and demonstrate how they can promote safety in real-world applications such as recidivism prediction, organ transplant prioritization, and content moderation.

Fairness studies of algorithmic decision-making systems often simplify complex decision processes, such as bail or loan approvals, into binary classification tasks. However, these approaches overlook that such decisions are not inherently binary (e.g., approve or not approve bail or loan); they also involve non-binary treatment decisions (e.g., bail conditions or loan terms) that can influence the downstream outcomes (e.g., loan repayment or reoffending). In this paper, we argue that non-binary treatment decisions are integral to the decision process and controlled by decision-makers and, therefore, should be central to fairness analyses in algorithmic decision-making. We propose a causal framework that extends fairness analyses and explicitly distinguishes between decision-subjects' covariates and the treatment decisions. This specification allows decision-makers to use our framework to (i) measure treatment disparity and its downstream effects in historical data and, using counterfactual reasoning, (ii) mitigate the impact of past unfair treatment decisions when automating decision-making. We use our framework to empirically analyze four widely used loan approval datasets to reveal potential disparity in non-binary treatment decisions and their discriminatory impact on outcomes, highlighting the need to incorporate treatment decisions in fairness assessments. Moreover, by intervening in treatment decisions, we show that our framework effectively mitigates treatment discrimination from historical data to ensure fair risk score estimation and (non-binary) decision-making processes that benefit all stakeholders.

AI adoption is rapidly moving from an experiment to an essential part of business practices and planning. Across every industry, more use cases are being developed for AI to drive business efficiencies, optimize data, improve the customer experience and support business goals and initiatives. Companies looking to mature their AI programs should keep an eye out for these important conversations around the adoption of AI tools and technology. Machine learning is a valuable tool that has benefited businesses for decades but only became widely popular recently. In this new era of digital acceleration, companies are looking for ways to drive efficiency in their companies--and for many, the answer lies in automation.

It seems every day we are inching closer to an efficient AGI. Just this morning I saw on twitter a post in which GPT-3 generated a full 90's era rap song advertising a blockchain with extreme accuracy and precision of language. If you had given me 3 hours I still doubt I could've done a better job. It's more obvious than ever, with digital images of every style, and soon essays and songs, that we are outsourcing our creativity to AI. It won't be long before an AGI can accomplish any creative task that we could dream of.

Martin Birch, CEO and president of ibml, has 20 years of experience as a global leader in the intelligent information management industry. AI adoption is rapidly moving from an experiment to an essential part of business practices and planning. Across every industry, more use cases are being developed for AI to drive business efficiencies, optimize data, improve the customer experience and support business goals and initiatives. Companies looking to mature their AI programs should keep an eye out for these important conversations around the adoption of AI tools and technology. Machine learning is a valuable tool that has benefited businesses for decades but only became widely popular recently.

The Impact of AI and Machine Learning on our Jobs Over the past year there have been an increasing number of articles written about jobs that can be done by a machine versus a person. I tend to be pretty optimistic about the future, but I don't believe anyone can know how the nature of jobs will be transformed as automation is introduced into various aspects of life. Here's an article from Fast Company that appeared 3 1/2 years ago about the changes coming from machine learning and artificial intelligence technologies. I don't think it's aged well. Here are three things it listed: 1) Unstructured problem-solving: solving for problems in which the rules do not currently exist.