Viljanen, Mika, Technology matters: how algorithm and artificial intelligent technology features affect harms reduction efforts

8 important questions on Viljanen, Mika, Technology matters: how algorithm and artificial intelligent technology features affect harms reduction efforts


Technological Impact on Harm Reduction: Discuss the impact of technological agency in AAI systems on harm production dynamics, exploring both opportunities and risks. Provide examples to support your arguments.

Technological Impact on Harm Reduction: Discuss how technological agency allows for intentional harm reduction through design. Risks: Explore the potential pitfalls, such as the loss of human safeguards and the need for robust social harm reduction design.


Uninterpretability and Analytical Tracing: Explore the implications of uninterpretability in AAI systems for harm reduction efforts, particularly in terms of analytical tracing of system logics. Discuss potential tools and methodologies that can aid in overcoming these challenges.

Uninterpretability and Analytical Tracing: Examine how uninterpretability complicates analytical tracing and understanding system logics. Explore post hoc interpretation tools and virtual testing environments as potential solutions.


Non-linear Performance and Outcome Harms: Evaluate the relationship between non-linear performance in AAI systems and the landscape of outcome harms. Discuss the limitations of localized tests in anticipating system responses and propose strategies for mitigating these challenges.

Non-linear Performance and Outcome Harms: Discuss how non-linear performance contributes to unpredictable outcome harms. Propose strategies like large-scale simulation environments to address challenges in anticipating system responses.
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Dynamicity and Lifelong Learning: Analyze the revolutionary shift introduced by dynamic AAI systems engaging in continuous lifelong learning. Discuss the challenges in preempting outcome harm, ensuring legitimacy, and controlling ontological harms in such systems.

Dynamicity and Lifelong Learning: Examine the challenges posed by dynamic AAI systems engaging in continuous lifelong learning. Discuss the importance of monitoring tools and cautious deployment in contexts with low potential for significant harms.


Ethical Considerations in AAI Development: Critically assess the ethical problems in algorithmic systems and AI applications that have triggered academic debates. Discuss the ethical considerations that designers and regulators should prioritize in the development and deployment of AAI technologies.

Ethical Considerations in AAI Development: Analyze the ethical problems triggering academic debates in algorithmic systems and AI applications. Discuss key ethical considerations that designers and regulators should prioritize in AAI development.


Role of Humans in AAI Decision-making: Compare and contrast the roles of humans in decision-making and decision support in AAI systems. Evaluate the effectiveness of human-in-the-loop or human-on-the-loop safeguards in controlling AAI technologies and discuss their limitations.

Role of Humans in AAI Decision-making: Compare and contrast the roles of humans in decision-making and decision support. Evaluate the effectiveness of human-in-the-loop or human-on-the-loop safeguards, considering potential complacency and skill loss.


Post hoc Interpretation Tools and System Justification: Investigate the role of post hoc interpretation tools in analyzing AAI systems' logic and justifying their outcomes. Discuss how these tools contribute to harm reduction efforts and their potential impact on system legitimacy.

Post hoc Interpretation Tools and System Justification: Explore the role of post hoc interpretation tools in analyzing system logic and justifying outcomes. Discuss how these tools contribute to harm reduction and their potential influence on system legitimacy.


Determinacy and System Reliability: Assess the importance of determinacy in AAI systems for stabilizing designers' anticipatory capabilities. Discuss the implications for system reliability and explore engineering efforts to address issues related to system decay and software-induced indeterminacy.

Determinacy and System Reliability: Assess the importance of determinacy in stabilizing designers' anticipatory capabilities.Discuss engineering efforts, such as redundancy and resilience, to address issues related to system decay and software-induced indeterminacy.

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