This study tries to answer these questions and investigates the ethical challenges of ADM (Algorithm Decision Making) arisen in the context of ethical decision-making and their ethical outcomes.
Algorithms and algorithmic decision-making simplify and improve people's lives and create new values for our society. ADM is used by banks to establish creditworthiness or by the police to calculate the likelihood of future crimes. It is used in agriculture, aviation, the insurance industry, and many other business areas. ADM is becoming increasingly essential and plays a crucial role in our daily life.
However, the use of algorithms is not just about opportunities and benefits. ADM hides significant risks and creates further ethical challenges. The ethics of algorithms is a provocative topic that causes a lot of controversies. As well as the application of ADM in the ethical context. Is it ethical to use algorithmic decision-making? Is the decision-maker "moral aware" of applying ADM?. Does moral awareness about the ethical implications of ADM always lead to ethical decisions?
Table of Contents
1. Introduction
2. Algorithmic decision making
2.1. Algorithmic selection & algorithmic decision making
2.2. Use of algorithmic decision making and their risks
3. Algorithmic decision making, ethical challenges and outcomes in the context of ethical decision making
3.1. Moral awareness and ethical challenges of ADM
3.2. Moral decision making and ethical outcomes
3.3. Amoral decision making and ethical outcomes
4. Conclusion
Objectives and Core Themes
This study explores the ethical implications of Algorithmic Decision Making (ADM) within professional contexts, specifically analyzing how algorithmic systems influence decision-making processes. By applying the Model of Tenbrunsel and Smith-Crowe, the research investigates whether decision makers remain "morally aware" when utilizing complex, automated systems and what the resulting ethical outcomes of these interactions are.
- Theoretical foundations of algorithmic selection and decision making.
- Categorization of risks and ethical challenges inherent in ADM systems.
- The relationship between moral awareness and algorithmic usage in business.
- Distinctions between moral and amoral decision-making processes in the presence of ADM.
- Analysis of intended and unintended ethical outcomes in automated decision environments.
Excerpt from the Book
3.1. Moral awareness and ethical challenges of ADM
Moral awareness is ‘a critical component of ethical decision making’ and indicates ‘whether decision makers are morally aware’ (Tenbrunsel/ Smith-Crowe 2008: 555). Existence or absence of moral awareness does not tell if the decision maker is a do-gooder (Tenbrunsel/ Smith-Crowe 2008: 553). It tells if he is aware of ethical implications or not (ibid.). This component determines if the decision maker is involved in a ‘moral decision making’ or ‘amoral decision making’ process (ibid.).
Moral awareness is directly connected with the ‘notion of attention’ (Reynolds 2006: 233). Identifying an ‘issue as a moral issue’ (Reynolds 2006: 234) or ‘an ethical dilemma’ (Tenbrunsel/ Smith-Crowe 2008: 556) distinguishes an ordinary decision maker from a ‘moral aware’ decision maker (Reynolds 2006: 234; Tenbrunsel/ Smith-Crowe 2008: 556; Jones 1991: 367). He recognizes that his conscious choice could have harmful consequences for others (Jones 1991: 367; Reynolds 2006: 233 f.). ‘The presence of harm and the violation of a behavioral norm’ are inherent components of moral awareness (Reynolds 2006: 234).
Summary of Chapters
1. Introduction: This chapter introduces the societal role of algorithms, defines ADM, and outlines the study's research objectives centered on ethical decision-making frameworks.
2. Algorithmic decision making: This section defines the sociotechnical construct of "algorithmic selection" and examines the primary types, application areas, and inherent risks of ADM.
3. Algorithmic decision making, ethical challenges and outcomes in the context of ethical decision making: This chapter applies the Model of Tenbrunsel and Smith-Crowe to analyze how moral awareness, or the lack thereof, impacts the ethics of decisions assisted by algorithms.
4. Conclusion: The concluding chapter summarizes the key findings regarding the moral challenges of ADM and suggests avenues for future research into algorithmic accountability and ethical compliance.
Keywords
Algorithmic Decision Making, ADM, Ethical Decision Making, Moral Awareness, Algorithmic Selection, Digital Discrimination, Business Ethics, Artificial Intelligence, Automation, Transparency, Accountability, Moral Agency, Data-Driven Decisions, Ethical Outcomes, Algorithmic Risk.
Frequently Asked Questions
What is the primary focus of this paper?
The paper examines the intersection of algorithmic decision-making (ADM) and ethical decision-making processes, specifically analyzing how ADM influences the moral choices of human agents.
Which theoretical model is used to analyze the topic?
The study primarily utilizes the Model of Tenbrunsel and Smith-Crowe, which breaks down ethical decision-making into moral awareness, moral decision-making, and amoral decision-making.
How is the term "moral awareness" defined in this context?
Moral awareness refers to a decision maker's capacity to recognize an issue as having ethical implications and understanding that their choices could cause harm to others.
What is the significance of the distinction between fully-automated and part-automated ADM?
The study focuses on part-automated ADM because it involves human participation, thus requiring the human agent to function as a "moral agent" who makes the final decision.
What are the main risks associated with ADM discussed in the text?
Key risks include a lack of transparency, the potential for digital discrimination (such as bias in hiring processes), and the unintended ethical consequences arising from the opacity of complex algorithms.
Does the author argue that algorithms are objective?
No, the author argues that the perception of algorithms as purely objective mathematical constructs is often an error caused by a lack of understanding regarding their functionality and inherent biases.
How does the "complexity of algorithms" affect moral awareness?
The complexity and opacity of ADM systems make it difficult for standard users to identify ethical implications, often causing them to default to amoral decision-making processes based on economic efficiency rather than ethical considerations.
What role does the HR department play as a case study?
The HR sector is used to illustrate how algorithmic filtering in hiring can lead to discrimination based on geography, gender, or ethnicity when the decision maker fails to critically assess the algorithmic output.
What does the author suggest for future research?
The author calls for more empirical experiments on how human decision makers interact with ADM, the development of ethical compliance rules for businesses, and further investigation into the ethical obligations of algorithm developers.
What is "unintentionally unethical" behavior?
This occurs when a decision maker believes their criteria are neutral or correct, yet the final outcome leads to negative ethical consequences, often because the user did not recognize the biased nature of the algorithmic input.
- Citar trabajo
- Anastasia Kabushka (Autor), 2019, Ethics of Algorithms. Ethical Challenges and Outcomes of Algorithmic Decision-Making, Múnich, GRIN Verlag, https://www.grin.com/document/1031107
