Algorithmic Impact Assessment Overview

<p>By making Algorithmic Impact Assessments publicly available. (A)</p> Signup and view all the answers

<p>Combatting discrimination in mortgage lending through AI. (D)</p> Signup and view all the answers

<p>The algorithm may inadvertently discriminate against transgender travelers. (B)</p> Signup and view all the answers

<p>Their disability-specific access needs can be misinterpreted as suspicious activity. (A)</p> Signup and view all the answers

<p>Algorithms may perpetuate existing health inequities. (A)</p> Signup and view all the answers

<p>To create a framework for the development of ethical and unbiased AI systems. (A)</p> Signup and view all the answers

<p>Ensuring equitable access and outcomes. (B)</p> Signup and view all the answers

<p>Algorithmic discrimination is a serious issue that needs to be addressed. (A)</p> Signup and view all the answers

<p>To identify and mitigate potential biases and unfair outcomes. (D)</p> Signup and view all the answers

<p>Reduction in job opportunities. (C)</p> Signup and view all the answers

<p>Asian Americans and Pacific Islanders (B)</p> Signup and view all the answers

<p>Civil rights, equal opportunities, and access to critical resources (A)</p> Signup and view all the answers

<p>Protection from excessive punishment (D)</p> Signup and view all the answers

<p>Data generated by technologies used in a sensitive domain (B)</p> Signup and view all the answers

<p>Data related to interaction with the criminal justice system (D)</p> Signup and view all the answers

<p>A loss of privacy or financial harm due to identity theft (B)</p> Signup and view all the answers

<p>Potentially high-risk inputs that may lead to problems (D)</p> Signup and view all the answers

<p>It can result in the spreading and scaling of harms. (A)</p> Signup and view all the answers

<p>Yes, data concerning a minor is always considered sensitive (B)</p> Signup and view all the answers

<p>It should be subject to extra oversight. (B)</p> Signup and view all the answers

<p>Data about data, such numerical or text information. (B)</p> Signup and view all the answers

<p>Using aggregated datasets where possible. (C)</p> Signup and view all the answers

<p>Independent evaluation, such as via APIs. (A)</p> Signup and view all the answers

<p>Ensuring built-in protections and user agency over data use. (B)</p> Signup and view all the answers

<p>When it is legally authorized, benefits those impacted, and risks are mitigated. (B)</p> Signup and view all the answers

<p>It should be clearly labeled to identify contexts for limited reuse. (B)</p> Signup and view all the answers

<p>Obfuscating user choices with privacy-invasive defaults. (A)</p> Signup and view all the answers

<p>Collecting only data strictly necessary for the specific context. (C)</p> Signup and view all the answers

<p>It should be identified and tracked, e.g., via type in a data schema. (B)</p> Signup and view all the answers

<p>Making requests concise, understandable, and giving users agency over data use. (A)</p> Signup and view all the answers

<p>To avoid continuous surveillance in areas such as education, work, and housing. (C)</p> Signup and view all the answers

<p>Applying enhanced restrictions and ethical review of data usage. (C)</p> Signup and view all the answers

<p>Subjecting it to pre-deployment assessments and limits to protect privacy and civil liberties. (C)</p> Signup and view all the answers

<p>Only in cases where the user can give consent in a meaningful way. (B)</p> Signup and view all the answers

<p>To oversee coordination and advise on the DOE AI Strategy (D)</p> Signup and view all the answers

<p>U.S. Intelligence Community (B)</p> Signup and view all the answers

<p>Development of safe autonomous and cyber physical systems (C)</p> Signup and view all the answers

<p>Stronger transparency and validity requirements (A)</p> Signup and view all the answers

<p>Developing safe, trustworthy, fair, and explainable AI systems (D)</p> Signup and view all the answers

Protecting individuals from excessive or inappropriate data use through safeguards built into systems and giving them control over their data.

Designing systems that ensure privacy is considered from the beginning, not as an afterthought.

Data should only be collected if it's absolutely necessary for the specific purpose of the system or service.

Users should understand and agree to how their data is used, and their choices should be clear and easy to make.

Especially sensitive data, like health, finance, or education records, should be protected with extra safeguards and only used for necessary purposes.

Surveillance technology needs careful oversight to prevent abuse and protect privacy and civil liberties.

Continuous monitoring technologies, like in education or workplaces, should be limited to avoid limiting rights or opportunities.

Privacy practices need to be updated to address new challenges and technologies.

Groups that have historically been denied fair and equal treatment due to their identity or circumstances.

Rights, opportunities, or access to essential resources, services, and protections, covering areas like civil rights, education, employment, healthcare, and financial services.

Data about individuals that relates to sensitive areas like health, finances, location, or criminal justice interactions. It can also include data that could potentially be used in ways that harm individuals.

Areas where data and metadata are considered especially sensitive, such as health, finances, location, or criminal justice.

Data generated by or about individuals under the age of legal adulthood, even if not related to sensitive domains. It could be numerical, text, image, audio, or video data.

Information about a person's genes or biological characteristics.

Data about a person's physical characteristics, like fingerprints or facial recognition.

Data about a person's behavior or patterns, like website visits, app usage, or shopping habits.

Data derived from algorithms, such as outputs of previous models. It should be identified and tracked, as it can lead to feedback loops, harm, or inaccurate results.

Using data in a different context can spread harm. Data from sensitive areas, like criminal justice or finance, requires stricter rules for re-use.

Systems should be designed to allow independent experts to evaluate their performance and safety. This is done through APIs, which allow external testing.

A group of experts within the Department of Energy that guides the ethical development and use of AI.

A set of principles created by the US Department of Defense that guide the ethical development and use of AI in national security and defense.

A set of guidelines developed by the US Intelligence Community to ensure the ethical development and use of AI in intelligence gathering.

A framework developed by the US Intelligence Community to help put the ethical principles for AI into practice.

The National Science Foundation funds research on developing safe, secure, and effective automated systems that adhere to ethical AI principles.

A process used to assess the potential risks and impacts of an automated system, including its fairness, equity, and potential for bias.

Examining the design and implementation of an automated system to identify and address potential disparities or biases that might impact different groups.

Testing the system with data from different groups to measure potential bias and unfair outcomes.

Making the results of the algorithmic impact assessment available to the public to promote transparency and accountability.

The process of correcting any identified biases or disparities in an automated system.

Automated systems should be evaluated to ensure they are free from bias before being sold or used.

The design of automated systems should prioritize fairness and equity for all users.

Certain types of algorithmic discrimination are already illegal under existing laws against discrimination.

TSA's plan to implement a gender-neutral algorithm aims to reduce bias in airport security screenings.

Individuals with disabilities may be disproportionately flagged as suspicious by AI systems due to their accessibility needs.

An algorithm used to assess healthcare needs unfairly ranked Black patients as lower need compared to white patients with similar conditions.

Healthcare algorithms sometimes use demographic variables that adjust results based on race or ethnicity, potentially leading to inequitable care.

Automated systems must be developed with clear expectations and guidelines to prevent discrimination.