Machine Learning ML agents have been increasingly used in decision-making across a wide range of tasks and environments. These ML agents are typically designed to balance multiple objectives when making choices. Understanding how their decision-making processes align with or diverge from human reasoning is essential. Human agents often encounter hard choices, that is, situations where options are incommensurable; neither option is preferred, yet the agent is not indifferent between them. In such cases, human agents can identify hard choices and resolve them through deliberation. In contrast, current ML agents, due to fundamental limitations in Multi-Objective Optimisation or MOO methods, cannot identify hard choices, let alone resolve them. Neither Scalarised Optimisation nor Pareto Optimisation, the two principal MOO approaches, can capture incommensurability. This limitation generates three distinct alignment problems: the alienness of ML decision-making behaviour from a human perspective; the unreliability of preference-based alignment strategies for hard choices; and the blockage of alignment strategies pursuing multiple objectives. Evaluating two potential technical solutions, I recommend an ensemble solution that appears most promising for enabling ML agents to identify hard choices and mitigate alignment problems. However, no known technique allows ML agents to resolve hard choices through deliberation, as they cannot autonomously change their goals. This underscores the distinctiveness of human agency and urges ML researchers to reconceptualise machine autonomy and develop frameworks and methods that can better address this fundamental gap.

ESPN personality and OutKick's Clay Travis talk about who the pundit will vote for in the 2024 presidential election. ESPN star Stephen A. Smith snapped back at former Democrat presidential nominee Hillary Clinton, who told voters to "get over yourselves" when asked about Americans dreading a Trump-Biden rematch this November. Clinton made her declaration in an appearance on Monday's "The Tonight Show." She suggested it wasn't a hard choice to make for voters because "one is old, and effective, and compassionate, has a heart and really cares about people. And one is old and has been charged with 91 felonies."

As AI systems are integrated into high stakes social domains, researchers now examine how to design and operate them in a safe and ethical manner. However, the criteria for identifying and diagnosing safety risks in complex social contexts remain unclear and contested. In this paper, we examine the vagueness in debates about the safety and ethical behavior of AI systems. We show how this vagueness cannot be resolved through mathematical formalism alone, instead requiring deliberation about the politics of development as well as the context of deployment. Drawing from a new sociotechnical lexicon, we redefine vagueness in terms of distinct design challenges at key stages in AI system development. The resulting framework of Hard Choices in Artificial Intelligence (HCAI) empowers developers by 1) identifying points of overlap between design decisions and major sociotechnical challenges; 2) motivating the creation of stakeholder feedback channels so that safety issues can be exhaustively addressed. As such, HCAI contributes to a timely debate about the status of AI development in democratic societies, arguing that deliberation should be the goal of AI Safety, not just the procedure by which it is ensured.

Artificial intelligence (AI) is supposed to help us make better choices. Some of these choices are small, like what route to take to work, or what music to listen to. Others are big, like what treatment to administer for a disease or how long to sentence someone for a crime. If AI can assist with these big decisions, we might think it can also help with hard choices, cases where alternatives are neither better, worse nor equal but on a par. The aim of this paper, however, is to show that this view is mistaken: the fact of parity shows that there are hard limits on AI in decision making and choices that AI cannot, and should not, resolve.

As AI systems become prevalent in high stakes domains such as surveillance and healthcare, researchers now examine how to design and implement them in a safe manner. However, the potential harms caused by systems to stakeholders in complex social contexts and how to address these remains unclear. In this paper, we explain the inherent normative uncertainty in debates about the safety of AI systems. We then address this as a problem of vagueness by examining its place in the design, training, and deployment stages of AI system development. We adopt Ruth Chang's theory of intuitive comparability to illustrate the dilemmas that manifest at each stage. We then discuss how stakeholders can navigate these dilemmas by incorporating distinct forms of dissent into the development pipeline, drawing on Elizabeth Anderson's work on the epistemic powers of democratic institutions. We outline a framework of sociotechnical commitments to formal, substantive and discursive challenges that address normative uncertainty across stakeholders, and propose the cultivation of related virtues by those responsible for development.

We heard a lot about AI and machine learning at the Google I/O developers conference keynote May 9, but there was one word that didn't make an appearance on a slide: privacy. Unlike its heavyweight peers, Google didn't use its yearly spotlight to announce any changes to the way it tracks and collects your data. If anything, it's doubling down on data collection with things like the Google Duplex project, which uses your phone to make Assistant-powered calls in the real-world. While Facebook is trying to salvage its image in the wake of the Cambridge Analytica scandal and Apple is positioning privacy as a "fundamental human right," Google continues to walk a fine line between protecting and profiling our data. Google makes no secret of the importance of data in its machine learning and artificial intelligence projects.

Ma said the emerging opportunities -- and risks -- from artificial intelligence and globalization are two of the topics that keep him on the road. "This is why I'm traveling, talking to all the government and state leaders and telling them move fast. If they do not move fast, there's going to be trouble," Ma said. "So when we see something is coming, we have to prepare now. My belief is that you have to repair the roof while it is still functioning."

In Silicon Valley, many companies aspire to the ideal of an ethical company. You can see this in company mottos, such as "Don't Be Evil," or in the social responsibility efforts espoused by many peer tech companies. On a deeper level, though, the behavior of companies like Google, Facebook, LinkedIn, and others is increasingly governed by the machine-learned systems they build to run their businesses. These companies are now starting to ask themselves how they can make an informed decision about how they operate their machine learning systems in an ethical manner, instead of being driven solely by revenue or some more abstract success metric. But we, as developers, are not off the hook.