PDF(Pubmed)
Abstract：
Artificial intelligence (AI) has long been recognised as a challenge to responsibility. Much of this discourse has been framed around robots, such as autonomous weapons or self-driving cars, where we arguably lack control over a machine\'s behaviour and therefore struggle to identify an agent that can be held accountable. However, most of today\'s AI is based on machine-learning technology that does not act on its own, but rather serves as a decision-support tool, automatically analysing data to help human agents make better decisions. I argue that decision-support tools pose a challenge to responsibility that goes beyond the familiar problem of finding someone to blame or punish for the behaviour of agent-like systems. Namely, they pose a problem for what we might call \"decision ownership\": they make it difficult to identify human agents to whom we can attribute value-judgements that are reflected in decisions. Drawing on recent philosophical literature on responsibility and its various facets, I argue that this is primarily a problem of attributability rather than of accountability. This particular responsibility problem comes in different forms and degrees, most obviously when an AI provides direct recommendations for actions, but also, less obviously, when it provides mere descriptive information on the basis of which a decision is made.
摘要：
人工智能（AI）长期以来一直被认为是对责任的挑战。大部分演讲都是围绕机器人展开的，比如自动武器或自动驾驶汽车，在那里，我们可以说缺乏对机器行为的控制，因此很难确定一个可以被追究责任的代理人。然而,今天的大部分人工智能都是基于机器学习技术，而机器学习技术并不独立运作，而是作为决策支持工具，自动分析数据，以帮助人类代理人做出更好的决策。我认为，决策支持工具对责任构成了挑战，这超出了为类似代理人的系统的行为而责备或惩罚某人的熟悉问题。即,它们给我们所谓的“决策所有权”带来了一个问题：它们使我们难以识别我们可以将价值判断归因于哪些人类代理人，这些判断反映在决策中。借鉴最近关于责任及其各个方面的哲学文献，我认为，这主要是可归因性问题，而不是问责制问题。这个特殊的责任问题有不同的形式和程度，最明显的是，当人工智能为行动提供直接建议时，而且,不太明显，当它仅提供描述性信息时，就可以做出决定。
