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An Analysis of the Interaction Between Intelligent Software Agents and Human Users

机译：智能软件代理与人类用户互动的分析

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Interactions between an intelligent software agent (ISA) and a human user are ubiquitous in everyday situations such as access to information, entertainment, and purchases. In such interactions, the ISA mediates the user’s access to the content, or controls some other aspect of the user experience, and is not designed to be neutral about outcomes of user choices. Like human users, ISAs are driven by goals, make autonomous decisions, and can learn from experience. Using ideas from bounded rationality (and deploying concepts from artificial intelligence, behavioural economics, control theory, and game theory), we frame these interactions as instances of an ISA whose reward depends on actions performed by the user. Such agents benefit by steering the user’s behaviour towards outcomes that maximise the ISA’s utility, which may or may not be aligned with that of the user. Video games, news recommendation aggregation engines, and fitness trackers can all be instances of this general case. Our analysis facilitates distinguishing various subcases of interaction (i.e. deception, coercion, trading, and nudging), as well as second-order effects that might include the possibility for adaptive interfaces to induce behavioural addiction, and/or change in user belief. We present these types of interaction within a conceptual framework, and review current examples of persuasive technologies and the issues that arise from their use. We argue that the nature of the feedback commonly used by learning agents to update their models and subsequent decisions could steer the behaviour of human users away from what benefits them, and in a direction that can undermine autonomy and cause further disparity between actions and goals as exemplified by addictive and compulsive behaviour. We discuss some of the ethical, social and legal implications of this technology and argue that it can sometimes exploit and reinforce weaknesses in human beings.
机译：智能软件代理（ISA）与人类用户之间的交互在日常情况下无处不在，例如访问信息，娱乐和购买。在这种交互中，ISA会介导用户对内容的访问，或者控制用户体验的其他方面，并且不会被设计为对用户选择的结果保持中立。像人类用户一样，ISA受目标驱动，自主决策并可以从经验中学习。使用来自有限理性的思想（并部署来自人工智能，行为经济学，控制理论和博弈论的概念），我们将这些交互构图为ISA的实例，其奖励取决于用户执行的操作。通过将用户的行为引向最大化ISA效用的结果（可能与用户的效用或不与用户的效用一致），此类代理将从中受益。视频游戏，新闻推荐汇总引擎和健身跟踪器都可以成为这种一般情况的实例。我们的分析有助于区分交互的各种子情况（即欺骗，胁迫，交易和轻推），以及二阶效应，其中可能包括自适应界面诱发行为成瘾和/或用户信念变化的可能性。我们在概念框架内介绍了这些类型的交互，并回顾了有说服力的技术的当前示例以及因使用这些技术而引起的问题。我们认为，学习代理通常用于更新其模型和后续决策的反馈的性质可能会使人类用户的行为远离使他们受益的事物，并且会朝着破坏自治的方向发展，从而导致行动与目标之间的进一步差距。以上瘾和强迫行为为例。我们讨论了该技术的某些伦理，社会和法律含义，并认为该技术有时可以利用并强化人类的弱点。

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