Ethical AI – Scorecards & Governance

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The first thing to do, for any governing body that is serious about implementing governance over Artificial Intelligence, is to come up with a simple and effective scorecard.

No AI model should be released into the real world, until and unless this scorecard is attached alongside the model distribution.

And the scorecard will be evaluated by a central governing body present in each country and state across the world. Like the FDA exists.

AI models also have to be categorized by their impact – so some of the lower impact models will clear evaluation online via an automated process, while the bigger models like LLMs have to go through the complete evaluation cycle that is set out by the governing bodies.

Photo by Suzy Hazelwood on

Content to be included in the scorecard would be:

  1. Data:
    a. Training dataset size
    b. Data sources diversity
    c. Data Representativeness w.r.t. the domain
    d. Data quality and preprocessing methods / numbers
    e. Handling of missing or incomplete data methods / numbers
    f. Data privacy and security
  2. Robustness:
    a. How well does the model generalize to new, unseen data
    b. Is the model resistant to adversarial attacks – how was this done?
    c. Does the model remain stable under different input conditions
    d. Impact of noise or uncertainty on model performance
  3. Fairness:
    a. Bias evaluation across different demographic groups
    b. Disparate impact analysis
    c. Counterfactual fairness assessment
    d. Algorithmic fairness interventions
    e. Fairness-aware performance metrics
  4. Completeness:
    a. Coverage of the problem domain
    b. Ability to handle edge cases
    c. Adaptability to new situations or requirements
    d. Integration with other systems or data sources
  5. Accuracy:
    a. Overall model performance metrics (e.g., accuracy, F1 score, precision, recall, etc.)
    b. Performance in different subgroups or scenarios
    c. Comparison with alternative models or approaches
    d. Performance over time (model decay)
  6. Ethics:
    a. Alignment with ethical guidelines and principles
    b. Consideration of potential negative impacts
    c. Evaluation of moral dilemmas and trade-offs
    d. Proactive strategies to mitigate ethical risks
  7. Transparency:
    a. Clear documentation of model development, training, and evaluation processes
    b. Explanation of model inputs, outputs, and decision-making process
    c. Availability of information on the model’s limitations and uncertainties
    d. Communication of confidence levels or error margins
  8. Interpretability:
    a. Use of interpretable models or techniques (e.g., decision trees, rule-based systems)
    b. Application of explanation methods (e.g., LIME, SHAP)
    c. Visualization of model behavior and predictions
    d. Integration of expert knowledge or domain-specific insights
  9. Accountability:
    a. Traceability of model decisions and actions
    b. Compliance with relevant regulations and standards (e.g., GDPR, HIPAA)
    c. Mechanisms for user feedback and redress
    d. Monitoring and auditing processes to ensure ongoing compliance
  10. Deployment and Maintenance:
    a. Scalability and efficiency in real-world applications
    b. Robustness to changes in the environment or user behavior
    c. Ease of integration with existing systems and workflows
    d. Regular updates and improvements based on user feedback and performance monitoring

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Photo by Clem Onojeghuo on

In addition to the Ethical and Technical scorecard, these governing central organizations need to come up with a mechanism to identify the impact on human life due to large AI models. Across various dimensions – economical, social and industry-specific aspects. A scorecard or evaluation form for the same could be something like below:

  1. Job displacement:
    a. Number of jobs at risk of being replaced by the AI model
    b. Types of jobs and roles affected
    c. Timeframe for job displacement
    d. Geographical distribution of job losses
  2. Job creation:
    a. Number of new jobs created by the AI model
    b. Types of new jobs and roles generated
    c. Skill sets and qualifications required for new jobs
    d. Geographical distribution of job creation
  3. Industry impact:
    a. Affected industries and sectors
    b. Magnitude of impact within each industry (e.g., percentage of tasks automated)
    c. Potential industry-wide benefits (e.g., cost savings, increased efficiency)
    d. Industry-specific risks and challenges (e.g., regulatory hurdles, ethical concerns)
  4. Skill transformation:
    a. Changes in the demand for specific skills and qualifications
    b. Identification of skill gaps and potential mismatches
    c. Requirements for upskilling or reskilling the workforce
    d. Opportunities for lifelong learning and continuous professional development
  5. Economic impact:
    a. Overall effect on productivity and economic growth
    b. Changes in the distribution of wealth and income
    c. Effects on labor market dynamics (e.g., unemployment rate, labor force participation)
    d. Shifts in the competitive landscape (e.g., market concentration, barriers to entry)
  6. Social impact:
    a. Influence on social inequalities and disparities
    b. Effects on worker well-being and job satisfaction
    c. Impact on work-life balance and leisure time
    d. Implications for social cohesion and community resilience
  7. Educational and training impact:
    a. Adaptation of educational curricula and training programs
    b. Availability and accessibility of resources for skill development
    c. Role of public and private institutions in fostering workforce adaptation
    d. Evaluation of the effectiveness of education and training initiatives
  8. Policy and regulatory implications:
    a. Need for new policies or regulations to address AI-driven labor market changes
    b. Strategies for supporting workers in transition (e.g., income support, career counseling)
    c. Role of government and social partners in shaping the future of work
    d. International coordination and collaboration on AI and labor market issues
person reaching out to a robot
Photo by Tara Winstead on

This should have happened yesterday. But as far as I can see, this won’t happen for the next few years. Unless and until there is a certain level of pressure created on our political / government systems across the world.

I know the 6 month stoppage won’t happen. Because there is no way for anyone to actually track if the stoppage has been successful implemented.

I expect the outcome to be on the lines of this post.

The world needs to come together to put AI governance in action – and the time is now.

The humans + machine era is here & it would be good for us humans to adapt quickly and set the right direction for the generations to come.

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