Source-linked AI summary
Socially Responsible AI Algorithms: Issues, Purposes, and Challenges
Lu Cheng, Kush R. Varshney, Huan Liu
TL;DR
AI’s growing societal role brings both promise and risks, while existing responsible-AI solutions remain concentrated on fairness and bias in scoring or classification. This survey develops a systematic Socially Responsible AI Algorithms framework spanning AI’s interconnected technical and societal dimensions. It concludes that broader responsibility requires addressing ethical and philanthropic responsibilities alongside functional and legal obligations, while identifying substantial open challenges.
Problem
Existing responsible-AI solutions are narrow, focusing mainly on fairness and unwanted bias in scoring or classification algorithms despite AI’s broad societal effects.
Method
The survey defines Social Responsibility of AI, proposes a four-level responsibility pyramid, and organizes SRAs by their subjects, causes, means, objectives, and societal roles.
Results
The survey presents a systematic, full-scope framework connecting AI’s major dimensions and encompassing functional, legal, ethical, and philanthropic responsibilities.
Takeaways & Limitations
Socially responsible AI should address protection, information, and prevention or mitigation of negative impacts while considering responsibilities beyond algorithmic fairness.
Takeaways & Limitations
Current understanding of SRAs remains insufficient, while AI principles and policies can be vague and shaped mainly by researchers and powerful groups with mainstream populations in mind.
Abstract
from arXiv · showhide
In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. Discussions about whether we should (re)trust AI have repeatedly emerged in recent years and in many quarters, including industry, academia, healthcare, services, and so on. Technologists and AI researchers have a responsibility to develop trustworthy AI systems. They have responded with great effort to design more responsible AI algorithms. However, existing technical solutions are narrow in scope and have been primarily directed towards algorithms for scoring or classification tasks, with an emphasis on fairness and unwanted bias. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness and connect major aspects of AI that potentially cause AI's indifferent behavior. In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives. We further discuss how to leverage this framework to improve societal well-being through protection, information, and prevention/mitigation.
1. Introduction
AI is increasingly embedded across society, creating both significant benefits and risks while intensifying the need for socially responsible development. This survey argues that responsibility requires a broader framework than existing work focused mainly on fairness and bias in scoring or classification algorithms.
- AI affects healthcare, education, commerce, finance, criminal justice, and everyday life, making its social consequences broad and sociotechnical.
- Existing responsible-AI research emphasizes fair, transparent, accountable, and ethical scoring or classification algorithms intended to mitigate unwanted bias.
- The survey defines Social Responsibility of AI through principles, means, and objectives, and proposes four responsibilities: functional, legal, ethical, and philanthropic.
- Its SRAs framework organizes subjects, causes, means, and objectives while describing roles in protecting users, informing them, and preventing negative AI impacts.
- The survey identifies open problems ranging from why AI systems are needed to the development of new AI ethics principles and policies.
2. Social Responsibility of AI
Social Responsibility of AI is defined as a human value-driven process addressing technical and societal issues through socially responsible algorithms. Its pyramid organizes AI responsibilities as functional, legal, ethical, and philanthropic, while recognizing that these obligations remain dynamically connected.
- Social Responsibility of AI addresses both technical and societal issues through a framework intended to encompass the full range of AI responsibilities.
- The definition names fairness, transparency, accountability, reliability and safety, privacy and security, and inclusiveness as principles; SRAs are the means and shared societal value is the objective.
- The Pyramid of Social Responsibility of AI: The pyramid contains functional, legal, ethical, and philanthropic responsibilities, adapted from Carroll’s Pyramid of CSR.
- The Pyramid of Social Responsibility of AI: Functional competence forms the pyramid’s foundation, followed by expectations that AI operate efficiently and comply with law.
- The Pyramid of Social Responsibility of AI: The four responsibilities are separate concepts but not mutually exclusive, requiring technologists to reconcile obligations that remain in dynamic tension.
- Comparisons of Similar Concepts: Compared with similar concepts, Socially Responsible AI takes a systematic view that also includes functional, legal, and philanthropic responsibilities.
3. Socially Responsible AI Algorithms (SRAs)
Socially Responsible AI Algorithms prioritize stakeholders, especially minoritized and disadvantaged users, while pursuing just and trustworthy decisions. They combine user protection, information, prevention or mitigation of harm, and long-term beneficial impact through ongoing feedback.
- SRAs prioritize the needs of all stakeholders, especially minoritized and disadvantaged users, to make just and trustworthy decisions.
- Their obligations include protecting and informing users, preventing and mitigating negative impact, and maximizing long-term beneficial impact.
- SRAs continually receive user feedback to accomplish expected social values.
- SRAs integrate functional objectives, such as profit maximization, with societal objectives, such as transparency.
- The framework’s essentials center on ethical responsibilities, while its roles require ethical and philanthropic responsibilities.
3.1 Subjects of Socially Indifferent AI Algorithms
Socially indifferent AI algorithms can affect anyone, but minorities and disadvantaged groups often suffer most in frequency and severity. Individuals are also subjects when personal data are collected or used without meaningful control.
- Minorities and disadvantaged groups, including BIPOC people and females, can experience socially indifferent AI harms more frequently and severely.
- Reported examples include racial image mislabeling and job advertisements shown more often to males than females.
- Individuals become subjects when personal data are collected and used without consent or control over how, where, or by whom it is used.
3.2 Causes of Socially Indifferent AI Algorithms
The survey identifies formalization, measuring errors, bias, privacy, and correlation-versus-causation errors as causes of socially indifferent AI algorithms. These causes arise when context, representativeness, data quality, or causal structure are mishandled.
- The survey groups causes into formalization, measuring errors, bias, privacy, and correlation versus causation.
- Formalization can omit social and historical context during feature construction and introduce problems through unclear data-annotation criteria.
- 3.2.2 Measuring Errors: With non-representative samples, a learned model may achieve zero training error before deteriorating on later data because it does not represent the true model.
- 3.2.3 Bias: Heterogeneous, high-dimensional data can encourage overfitting and require more samples for generalization.
- 3.2.4 Data Misuse: Misuse of personal data without consent or awareness creates privacy and distrust problems.
- 3.2.5 Correlation vs Causation: Confounders can create spurious correlations between variables that are not causally connected.
3.3 Objectives of Socially Responsible AI Algorithms
The survey frames fairness, transparency, and safety as objectives for rebuilding trust in AI. It also emphasizes that transparency must be balanced with privacy and security, while safety includes accuracy, reliability, security, and robustness.
- Fairness, transparency, and safety are presented as core objectives for rebuilding trust in AI.
- 3.3.1 Fairness: Fairness is difficult to achieve because it is subjective, socially contextual, and evolving.
- 3.3.1 Fairness: A fair AI system in a specific context can still contribute to biased decisions because policymakers and environments also shape decision making.
- 3.3.2 Transparency: Transparency concerns understandable explanations of data, collection, algorithmic operation, and decisions, at global or local scope.
- 3.3.2 Transparency: Explanations can be hacked, and additional disclosures can increase vulnerability, making privacy and security necessary conditions for transparency.
- 3.3.3 Safety: Safety includes accuracy, reliability, security, and robustness as operational objectives.
3.4 Means Towards Socially Responsible AI Algorithms
The survey presents interpretability, adversarial machine learning, causal learning, and uncertainty quantification as means for developing socially responsible AI algorithms. These techniques support transparency, robustness, causal reasoning, and uncertainty-aware decision-making.
- The survey reviews interpretability, adversarial machine learning, causal learning, and uncertainty quantification as four primary techniques for achieving socially responsible AI goals.
- Interpretability and Explainability: Interpretability and explainability increase AI transparency by helping users understand model decisions, especially in high-stakes applications.
- Adversarial Machine Learning: Adversarial machine learning addresses robustness because crafted inputs can fool machine-learning models during testing or deployment.
- Causal Learning: Causal learning supports fairness, transparency, and robustness by modeling interventions, counterfactuals, and relationships expected to remain invariant across environments.
- Uncertainty Quantification: Uncertainty quantification treats uncertainty as a form of transparency that informs optimization and decision-making when deployment conditions differ from training data.
4. Roles of SRAs
Socially responsible AI algorithms should serve both ethical and philanthropic responsibilities. Ethical responsibilities come first because philanthropic benefits may be insignificant when ethical obligations fail.
- Socially responsible AI algorithms must address both ethical responsibilities and philanthropic responsibilities to support societal well-being.
- Ethical responsibilities should be ensured before philanthropic benefits because failures in ethical conduct can make those benefits insignificant.
4.1 Protecting
The protecting dimension shields people, especially vulnerable groups, from harm and negative AI impacts. It includes privacy-preserving mechanisms and data dignity, which gives users greater control over personal data.
- The protecting dimension shields humans, particularly vulnerable or at-risk people, from harm and negative impacts of AI systems.
- Privacy Preserving: Privacy concerns arise from illegitimate use and disclosure of sensitive data, motivating private aggregation, private training, and private inference mechanisms.
- Privacy Preserving: Context-free privacy methods assume worst-case dataset statistics and adversaries, whereas context-aware methods model the context in which data will be used.
- Data Dignity: Data dignity allows users to control how their data are used, negotiate usage terms, and retain autonomy over being found, analyzed, or forgotten.
- Data Dignity: Data-control models include systems designed to let users control personal data and participate in buying or selling it.
4.2 Informing
The informing dimension delivers timely information about AI’s potential negative results. The survey discusses disinformation, cyberbullying, and bias as informing applications with distinct detection challenges.
- The informing dimension delivers facts and information to users, especially about potential negative AI results, in a timely manner.
- Disinformation: Disinformation detection is difficult because false information spans domains, changes continuously, and creates evolving threats.
- Disinformation: Disinformation research uses textual, social, and knowledge-graph features, and also studies cross-domain detection, explanation, and causal dissemination.
- Cyberbullying: Cyberbullying detection must account for power imbalance and repeated aggressive acts, including their temporal dynamics.
- Bias: Bias detection faces limited access to decision data and algorithms, while model complexity can make systematic discrimination difficult to identify.
- Bias: Data exploration and group selection-rate comparisons provide tools for detecting potential data and algorithmic bias, including the ratio threshold τ = 0.8.
- Bias: Statistical disparity does not necessarily indicate discrimination because groups may differ in qualification rates.
- Bias: Regression analysis examines favorable or adverse decisions across groups using sensitive attributes, but unobserved decision factors limit interpretation.
4.3 Preventing/Mitigating
Prevention and mitigation address disinformation, cyberbullying, and bias through interventions that act on networks, content, users, and algorithm design.
- Disinformation Prevention/Mitigation: Disinformation mitigation includes early detection, network intervention, counter-cascades, and content flagging.Network intervention seeks a minimum set of nodes whose influence minimizes the original cascade.
- Disinformation Prevention/Mitigation: Network intervention can slow disinformation by influencing exposed users and inoculating many nodes in a short period.Approximation algorithms address the underlying NP-hard influence limitation or minimization problem.
- Cyberbullying Prevention/Mitigation: Cyberbullying prevention and mitigation can report, control, or warn about messages, support victims, and educate victims and bullies.Technological strategies also include parental controls, firewall blocking, service rules, and mobile controls.
- Bias Mitigation: Bias mitigation spans pre-processing, in-processing, and post-processing approaches, with fairness measures targeting individual, group, or subgroup fairness.Counterfactual fairness evaluates whether an individual’s decision would remain the same under a different sensitive attribute.
- Bias Mitigation: Fair classification methods require fairness definitions suited to context and include blinding and causal approaches.Algorithm operators are also encouraged to document research and develop bias impact statements to identify potential biases.
5. Open Problems and Challenges
The survey identifies unresolved challenges in making socially responsible AI effective across causal reasoning, social and algorithmic contexts, ethics policies, and human oversight.
- Causal Learning: Causal learning is presented as necessary for addressing robustness, explainability, and cause-effect obstacles created by correlation-based reasoning.The survey highlights intervention, counterfactuals, do-calculus, and propensity scoring as approaches used for fairness and interpretability.
- Context: Socially responsible AI interventions can become ineffective, inaccurate, or dangerously misguided when transferred into new social contexts.Fair ranking may increase minority exposure while remaining limited by employer gender preferences in particular job contexts.
- AI Ethics Principles and Policies: AI principles and policies are criticized for being vague and primarily shaped by researchers and powerful groups with mainstream populations in mind.Applied-ethics frameworks are proposed to operationalize principles and confront value trade-offs.
- Humans in the Loop: Fairness requirements can reduce prediction reliability and conflict across fairness notions, making human oversight important for high-stakes contextual decisions.Humans can help calibrate different fairness cutoffs for subgroups.
- Social Good Applications: Socially responsible AI can support fundraising through targeted donation solicitations and improve grant allocation by predicting project proposal success rates.These applications are associated with fundraising and greenlighting resources for non-profits, charities, universities, and other organizations.
6. Conclusion
The conclusion argues that socially responsible AI requires a broad, connected view of AI rather than a focus limited to scoring and classification algorithms.
- Conclusion: The survey defines Social Responsibility of AI through principles, means, and objectives, including fairness, inclusiveness, SRAs, and improving humanity.It also frames responsibility across functional, legal, ethical, and philanthropic levels.
- Conclusion: The proposed framework treats functional and societal aspects as integral parts of socially responsible AI algorithms.It focuses on how the framework can achieve Social Responsibility of AI.