This chapter examines the role of algorithms in decision-making across various fields, highlighting mixed public attitudes. **Algorithm aversion** is rooted in historical distrust and influenced by factors such as comfort with mathematics, education, and dispositional trust. People often mistrust algorithms due to their perceived lack of emotional capabilities and a desire for perfect forecasts. Errors made by algorithms are perceived more negatively than human errors, leading to decreased trust. Despite this, **algorithm appreciation** exists, particularly for objective tasks with quantifiable outcomes. Familiarity and transparency enhance trust in algorithms. The chapter also explores **robo-advisors**, digital interfaces providing automated investment advice. Trust and perceived expertise of the advisory firm are crucial for acceptance. Experimental studies show that the propensity to follow advice depends on alignment with self-directed choices and the advisor's gender. To overcome algorithm aversion, the chapter emphasizes developing **algorithmic literacy** among decision-makers, aligning human and algorithmic decision processes, and designing user-friendly interfaces. Context-specific behavioral design and transparent nudges can improve the utilization of algorithmic aids. The chapter provides insights into shaping public attitudes to enhance the acceptance and effective use of algorithmic decision-making tools. This chapter examines the role of algorithms in decision-making across various fields, highlighting mixed public attitudes. **Algorithm aversion** is rooted in historical distrust and influenced by factors such as comfort with mathematics, education, and dispositional trust. People often mistrust algorithms due to their perceived lack of emotional capabilities and a desire for perfect forecasts. Errors made by algorithms are perceived more negatively than human errors, leading to decreased trust. Despite this, **algorithm appreciation** exists, particularly for objective tasks with quantifiable outcomes. Familiarity and transparency enhance trust in algorithms. The chapter also explores **robo-advisors**, digital interfaces providing automated investment advice. Trust and perceived expertise of the advisory firm are crucial for acceptance. Experimental studies show that the propensity to follow advice depends on alignment with self-directed choices and the advisor's gender. To overcome algorithm aversion, the chapter emphasizes developing **algorithmic literacy** among decision-makers, aligning human and algorithmic decision processes, and designing user-friendly interfaces. Context-specific behavioral design and transparent nudges can improve the utilization of algorithmic aids. The chapter provides insights into shaping public attitudes to enhance the acceptance and effective use of algorithmic decision-making tools.
Do we like robot? Consumers' attitude towards the algorithm
Barbara Alemanni
2023-01-01
Abstract
This chapter examines the role of algorithms in decision-making across various fields, highlighting mixed public attitudes. **Algorithm aversion** is rooted in historical distrust and influenced by factors such as comfort with mathematics, education, and dispositional trust. People often mistrust algorithms due to their perceived lack of emotional capabilities and a desire for perfect forecasts. Errors made by algorithms are perceived more negatively than human errors, leading to decreased trust. Despite this, **algorithm appreciation** exists, particularly for objective tasks with quantifiable outcomes. Familiarity and transparency enhance trust in algorithms. The chapter also explores **robo-advisors**, digital interfaces providing automated investment advice. Trust and perceived expertise of the advisory firm are crucial for acceptance. Experimental studies show that the propensity to follow advice depends on alignment with self-directed choices and the advisor's gender. To overcome algorithm aversion, the chapter emphasizes developing **algorithmic literacy** among decision-makers, aligning human and algorithmic decision processes, and designing user-friendly interfaces. Context-specific behavioral design and transparent nudges can improve the utilization of algorithmic aids. The chapter provides insights into shaping public attitudes to enhance the acceptance and effective use of algorithmic decision-making tools. This chapter examines the role of algorithms in decision-making across various fields, highlighting mixed public attitudes. **Algorithm aversion** is rooted in historical distrust and influenced by factors such as comfort with mathematics, education, and dispositional trust. People often mistrust algorithms due to their perceived lack of emotional capabilities and a desire for perfect forecasts. Errors made by algorithms are perceived more negatively than human errors, leading to decreased trust. Despite this, **algorithm appreciation** exists, particularly for objective tasks with quantifiable outcomes. Familiarity and transparency enhance trust in algorithms. The chapter also explores **robo-advisors**, digital interfaces providing automated investment advice. Trust and perceived expertise of the advisory firm are crucial for acceptance. Experimental studies show that the propensity to follow advice depends on alignment with self-directed choices and the advisor's gender. To overcome algorithm aversion, the chapter emphasizes developing **algorithmic literacy** among decision-makers, aligning human and algorithmic decision processes, and designing user-friendly interfaces. Context-specific behavioral design and transparent nudges can improve the utilization of algorithmic aids. The chapter provides insights into shaping public attitudes to enhance the acceptance and effective use of algorithmic decision-making tools.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
