Principles for Algorithmic Transparency and Accountability 1. Awareness: Owners, designers, builders, users, and other stakeholders of...

  

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Principles for Algorithmic Transparency and Accountability 1. Awareness: Owners, designers, builders, users, and other stakeholders of analytic systems should be aware of the possible biases involved in their design, implementation, and use and the potential harm that biases can cause to individuals and society. 2. Access and Redress: Regulators should encourage the adoption of mechanisms that enable questioning and redress for individuals and groups that are adversely affected by algorithmically informed decisions. 3. Accountability: Institutions should be held responsible for decisions made by the algorithms that they use, even if it is not feasible to explain in detail how the algorithms produce their results. 4. Explanation: Systems and institutions that use algorithmic decision-making are encouraged to produce explanations regarding both the procedures followed by the algorithm and the specific decisions that are made. This is particularly important in public policy contexts. 5. Data Provenance: A description of the way in which the training data was collected should be maintained by the builders of the algorithms, accompanied by an exploration of the potential biases induced by the human or algorithmic data-gathering process. Public scrutiny of the data provides maximum opportu- nity for corrections. However, concerns over privacy, protecting trade secrets, or revelation of analytics that might allow malicious actors to game the system can justify restricting access to qualified and authorized individuals. 6. Auditability: Models, algorithms, data, and decisions should be recorded so that they can be audited in cases where harm is suspected. 7. Validation and Testing: Institutions should use rigorous methods to validate their models and document those methods and results. In particular, they should routinely perform tests to assess and determine whether the model generates discriminatory harm. Institutions are encouraged to make the results. of such tests public. Source: https://www.acm.org/binaries/content/assets/public-policy/2017_usacm_statement algorithms.pdf The Principles for Algorithmic Transparency and Accountability was established by the Association for Computing Machinery's U.S. Public Policy Council. The seven principles are included in the article "A Peek at Proprietary Algorithms." You were required to read the newspaper article "Sheriff Launched an Algorithm to Predict Who Might Commit a Crime. Dozens of People Said They Were Harassed by Deputies for No Reason," as well. Select two of the seven principles from the Principles for Algorithmic Transparency and Accountability and apply them to "Sheriff Launched an Algorithm... " article. How were these two principles violated or ignored by the Florida sheriff, and how should the sheriff address these violations? Principles for Algorithmic Transparency and Accountability 1. Awareness: Owners, designers, builders, users, and other stakeholders of analytic systems should be aware of the possible biases involved in their design, implementation, and use and the potential harm that biases can cause to individuals and society. 2. Access and Redress: Regulators should encourage the adoption of mechanisms that enable questioning and redress for individuals and groups that are adversely affected by algorithmically informed decisions. 3. Accountability: Institutions should be held responsible for decisions made by the algorithms that they use, even if it is not feasible to explain in detail how the algorithms produce their results. 4. Explanation: Systems and institutions that use algorithmic decision-making are encouraged to produce explanations regarding both the procedures followed by the algorithm and the specific decisions that are made. This is particularly important in public policy contexts. 5. Data Provenance: A description of the way in which the training data was collected should be maintained by the builders of the algorithms, accompanied by an exploration of the potential biases induced by the human or algorithmic data-gathering process. Public scrutiny of the data provides maximum opportu- nity for corrections. However, concerns over privacy, protecting trade secrets, or revelation of analytics that might allow malicious actors to game the system can justify restricting access to qualified and authorized individuals. 6. Auditability: Models, algorithms, data, and decisions should be recorded so that they can be audited in cases where harm is suspected. 7. Validation and Testing: Institutions should use rigorous methods to validate their models and document those methods and results. In particular, they should routinely perform tests to assess and determine whether the model generates discriminatory harm. Institutions are encouraged to make the results. of such tests public. Source: https://www.acm.org/binaries/content/assets/public-policy/2017_usacm_statement algorithms.pdf The Principles for Algorithmic Transparency and Accountability was established by the Association for Computing Machinery's U.S. Public Policy Council. The seven principles are included in the article "A Peek at Proprietary Algorithms." You were required to read the newspaper article "Sheriff Launched an Algorithm to Predict Who Might Commit a Crime. Dozens of People Said They Were Harassed by Deputies for No Reason," as well. Select two of the seven principles from the Principles for Algorithmic Transparency and Accountability and apply them to "Sheriff Launched an Algorithm... " article. How were these two principles violated or ignored by the Florida sheriff, and how should the sheriff address these violations?