Page 3 - AIWS Report

+ Building and use of AIs and AI-related activities aligned with the positive purposes of AI, listed above, are
encouraged and promoted.
+ By contrast, AIs and AI-related activities departing from the positive purpose, i.e. to have detrimental
effects on w
ell-being and happiness, to restrict the limitless possibility of humans, to provide less freedom to people with
constraints of resources and inflexible rules/processes, and to be the obstacles to solving the important issues
faced by mankind, such as SDGs, are discouraged and suppressed.

• Data sets: how to collect, where, whom, for what, by what. Data sets using for AI require accuracy,
validation and transparency.

+ Disclose what data they collect, from which sources, how data is collected (techniques & models) and will
be used, the purposes for which it is used.
+ Have methods to encourage the development of testing data sets.
+ Clearly communication and publicize governance programs or risk assessment tools to detect and remedy
any possible discriminatory effects of the data and models used.

• Algorithm: transparency, fairness, non-bias.

+ Accountable algorithms must follow proposed principles: responsibility, transparency, accuracy,
auditability, and fairness. The collection, use, and management of data by AI algorithms should follow ethical
principles that promote fairness and to avoid unjust impacts on people, particularly those related to sensitive
characteristics such as race, ethnicity, gender, nationality, income, sexual orientation, ability, and political or
religious belief.

• Intended impacts: for what, for whom, goals and purpose

+ Clearly communication about intended impacts of developing and using AI. Avoid AI implementation that
cause or contribute to adverse impacts to human.
+ Seek to prevent or mitigate adverse impacts to human. Provide for effective remediation of any impacts
caused by data practices.
+ Procure and deploy risk assessment tools to govern their adoption and create frameworks for assessing
impact.

• Transparency in national resources:

+ Publicize the identity of responsible departments for AI policy, and government AI projects'
implementation.

+ Publicize expenditures, the state budget on AI issues.
• Refrains from investing in harmful uses of AI:

The Michael Dukakis Institute for Leadership and Innovation
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