Decision theory and folk psychology both purport to represent the same phenomena: our belief-like and desire- and preference-like states. They also purport to do the same work with these representations: explain and predict our actions. But they do so with different sets of concepts. There's much at stake in whether one of these two sets of concepts can be accounted for with the other. Without such an account, we'd have two competing representations and systems of prediction and explanation, a dubious (...) dualism. Folk psychology structures our daily lives and has proven fruitful in the study of mind and ethics, while decision theory is pervasive in various disciplines, including the quantitative social sciences, especially economics, and philosophy. My interest is in accounting for folk psychology with decision theory -- in particular, for believe and wanting, which decision theory omits. Many have attempted this task for belief. (The Lockean Thesis says that there is such an account.) I take up the parallel task for wanting, which has received far less attention. I propose necessary and sufficient conditions, stated in terms of decision theory, for when you're truly said to want; I give an analogue of the Lockean Thesis for wanting. My account is an alternative to orthodox accounts that link wanting to preference (e.g. Stalnaker (1984), Lewis (1986)), which I argue are false. I argue further that want ascriptions are context-sensitive. My account explains this context-sensitivity, makes sense of conflicting desires, and accommodates phenomena that motivate traditional theses on which 'want' has multiple senses (e.g. all-things-considered vs. pro tanto). (shrink)

According to the Desiderative Lockean Thesis, there are necessary and sufficient conditions, stated in the terms of decision theory, for when one is truly said to want. What one is truly said to want, it turns out, varies remarkably by context—and to an underappreciated degree. To explain this context-sensitivity—and closure properties of wanting—I advance a Desiderative Lockean view that is distinctive in having two context-sensitive parameters.

What are the truth conditions of want ascriptions? According to an influential approach, they are intimately connected to the agent’s beliefs: ⌜S wants p⌝ is true iff, within S’s belief set, S prefers the p worlds to the not-p worlds. This approach faces a well-known problem, however: it makes the wrong predictions for what we call (counter)factual want ascriptions, wherein the agent either believes p or believes not-p—for example, ‘I want it to rain tomorrow and that is exactly what is (...) going to happen’ or ‘I want this weekend to last forever, but of course it will end in a few hours’. We solve this problem. The truth conditions for want ascriptions are, we propose, connected to the agent’s conditional beliefs. We substantiate this connection by pursuing a striking parallel between (counter)factual and non-(counter)factual want ascriptions on the one hand and counterfactual and indicative conditionals on the other. (shrink)

You want to see the concert, but don’t want to take a long drive (even though the concert is far away). Such *strongly conflicting desire ascriptions* are, I show, wrongly predicted incompatible by standard semantics. I then object to possible solutions, and give my own, based on *some-things-considered desire*. Considering the fun of the concert, but ignoring the drive, you want to see the concert; considering the boredom of the drive, but ignoring the concert, you don’t want to take the (...) drive. (shrink)

Algorithms wield increasing control over our lives—over which jobs we get, whether we're granted loans, what information we're exposed to online, and so on. Algorithms can, and often do, wield their power in a biased way, and much work has been devoted to algorithmic bias. In contrast, algorithmic neutrality has gone largely neglected. I investigate three questions about algorithmic neutrality: What is it? Is it possible? And when we have it in mind, what can we learn about algorithmic bias?

Fears of black-box algorithms are multiplying. Black-box algorithms are said to prevent accountability, make it harder to detect bias and so on. Some fears concern the epistemology of black-box algorithms in medicine and the ethical implications of that epistemology. In ‘Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI,' Durán and Jongsma seek to allay such fears. While some of their arguments are compelling, we still see reasons for fear.

The compelling, widely-accepted Satisfaction-is-Truth Principle says that if S wants p, then S has a desire that's satisfied in exactly the worlds where p is true. We reject the Principle; an agent may want p without having a desire that's satisfied when p obtains in any old way. Fara (2013) and Lycan (2012, ms) also reject the Principle, but they rely on contested intuitions about when agents get what they want. We instead appeal to—and shed new light on—the dispositional role (...) of desire. (shrink)

Meet Tega. Blue, fluffy, and AI-enabled, Tega is a relational robot: a robot designed to form relationships with humans. Created to aid in early childhood education, Tega talks with children, plays educational games with them, solves puzzles, and helps in creative activities like making up stories and drawing. Children are drawn to Tega, describing him as a friend, and attributing thoughts and feelings to him ("he's kind," "if you just left him here and nobody came to play with him, he (...) might be sad"). Scholars and members of the public alike have raised the alarm about relational robots, worrying that the relationships that people, and especially children, form with such robots are objectionably inauthentic. We, members of an interdisciplinary team that developed and studies Tega, make this inauthenticity worry precise and offer practical recommendations for addressing it. We distinguish two kinds of (in)authenticity -- inauthenticity as unreality, and inauthenticity as deception -- arguing that neither is just what others have thought it is. With our distinction in hand, we argue that the authenticity concern can be met only through co-design methods -- methods that give stakeholders of all kinds (e.g. parents, educators, and children themselves) a genuine say in how relational robots are built. (shrink)

A compositional semantics for anankastic conditionals (‘If you want p, you must φ’) has been elusive. Condoravdi & Lauer (2016) decisively object to all semantics that precede their own. C&L's view rests on a response to *the problem of conflicting goals*; C&L use an interpretation of 'want' on which an agent's desires don't conflict with her beliefs. But a proper response requires lack of conflict with the facts. C&L's view fails. Anankastic conditionals are still a mystery.