--- title: "Automated decision-making" chunk: 3/3 source: "https://en.wikipedia.org/wiki/Automated_decision-making" category: "reference" tags: "science, encyclopedia" date_saved: "2026-05-05T07:11:00.240751+00:00" instance: "kb-cron" --- === Transport and mobility === Connected and automated mobility (CAM) involves autonomous vehicles such as self-driving cars and other forms of transport which use automated decision-making systems to replace various aspects of human control of the vehicle. This can range from level 0 (complete human driving) to level 5 (completely autonomous). At level 5 the machine is able to make decisions to control the vehicle based on data models and geospatial mapping and real-time sensors and processing of the environment. Cars with levels 1 to 3 are already available on the market in 2021. In 2016 The German government established an 'Ethics Commission on Automated and Connected Driving' which recommended connected and automated vehicles (CAVs) be developed if the systems cause fewer accidents than human drivers (positive balance of risk). It also provided 20 ethical rules for the adaptation of automated and connected driving. In 2020 the European Commission strategy on CAMs recommended that they be adopted in Europe to reduce road fatalities and lower emissions however self-driving cars also raise many policy, security and legal issues in terms of liability and ethical decision-making in the case of accidents, as well as privacy issues. Issues of trust in autonomous vehicles and community concerns about their safety are key factors to be addressed if AVs are to be widely adopted. === Surveillance === Automated digital data collections via sensors, cameras, online transactions and social media have significantly expanded the scope, scale, and goals of surveillance practices and institutions in government and commercial sectors. As a result there has been a major shift from targeted monitoring of suspects to the ability to monitor entire populations. The level of surveillance now possible as a result of automated data collection has been described as surveillance capitalism or surveillance economy to indicate the way digital media involves large-scale tracking and accumulation of data on every interaction. == Ethical and legal issues == There are many social, ethical and legal implications of automated decision-making systems. Concerns raised include lack of transparency and contestability of decisions, incursions on privacy and surveillance, exacerbating systemic bias and inequality due to data and algorithmic bias, intellectual property rights, the spread of misinformation via media platforms, administrative discrimination, risk and responsibility, unemployment and many others. As ADM becomes more ubiquitous there is greater need to address the ethical challenges to ensure good governance in information societies. ADM systems are often based on machine learning and algorithms which are not easily able to be viewed or analyzed, leading to concerns that they are 'black box' systems which are not transparent or accountable. A report from Citizen Lab in Canada argues for a critical human rights analysis of the application of ADM in various areas to ensure the use of automated decision-making does not result in infringements on rights, including the rights to equality and non-discrimination; freedom of movement, expression, religion, and association; privacy rights and the rights to life, liberty, and security of the person. Legislative responses to ADM include: The European General Data Protection Regulation (GDPR), introduced in 2016, is a regulation in EU law on data protection and privacy in the European Union (EU). Article 22(1) enshrines the right of data subjects not to be subject to decisions, which have legal or other significant effects, being based solely on automatic individual decision making. GDPR also includes some rules on the right to explanation however the exact scope and nature of these is currently subject to pending review by the Court of Justice of the European Union. These provisions were not first introduced in the GDPR, but have been present in a similar form across Europe since the Data Protection Directive in 1995, and the 1978 French law, the loi informatique et libertés. Similarly scoped and worded provisions with varying attached rights and obligations are present in the data protection laws of many other jurisdictions across the world, including Uganda, Morocco and the US state of Virginia. Rights for the explanation of public sector automated decisions forming 'algorithmic treatment' under the French loi pour une République numérique. === Bias === ADM may incorporate algorithmic bias arising from: Data sources, where data inputs are biased in their collection or selection Technical design of the algorithm, for example where assumptions have been made about how a person will behave Emergent bias, where the application of ADM in unanticipated circumstances creates a biased outcome === Explainability === Questions of biased or incorrect data or algorithms and concerns that some ADMs are black box technologies, closed to human scrutiny or interrogation, has led to what is referred to as the issue of explainability, or the right to an explanation of automated decisions and AI. This is also known as Explainable AI (XAI), or Interpretable AI, in which the results of the solution can be analysed and understood by humans. XAI algorithms are considered to follow three principles - transparency, interpretability and explainability. === Information asymmetry === Automated decision-making may increase the information asymmetry between individuals whose data feeds into the system and the platforms and decision-making systems capable of inferring information from that data. On the other hand it has been observed that in financial trading the information asymmetry between two artificial intelligent agents may be much less than between two human agents or between human and machine agents. A research validated Daniel Kahneman's theory on noisy decisions by human experts in finance. It demonstrates the inherent inconsistencies in human judgments, which consequently affect the outcomes of automated decisions made by AI decision-support systems. == Research fields == Many academic disciplines and fields are increasingly turning their attention to the development, application and implications of ADM including business, computer sciences, human computer interaction (HCI), law, public administration, and media and communications. The automation of media content and algorithmically driven news, video and other content via search systems and platforms is a major focus of academic research in media studies. The ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT) was established in 2018 to study transparency and explainability in the context of socio-technical systems, many of which include ADM and AI. Key research centres investigating ADM include: Algorithm Watch, Germany ARC Centre of Excellence for Automated Decision-Making and Society, Australia Citizen Lab, Canada Informatics Europe == See also == Automated decision support Algorithmic bias Decision-making software Decision Management Ethics of artificial intelligence Government by algorithm Machine learning Recommender systems == References ==