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Responsible and Ethical AI Tenets

Ethical AI
November 15, 2021

AI is capable of limitless potential that is rapidly transforming our society as we know it. But as the saying goes – “with great power comes great responsibility” and additional risks. AI is no exception here – a strong ethical framework is critical to govern and monitor this powerful technology. So, how can AI be risky? AI depends on large volumes of data, and when this sensitive data, along with AI, are used for business-critical decisions, we must understand and be certain about what AI is doing with the data and why. Is it making accurate and unbiased decisions based on the data? Is it protecting individual privacy? The list goes on. A clear and defined commitment to ethical AI by every company and organization is the only means to mitigate the risks associated with misconduct.

What is ethical AI?

Ethical AI monitors and analyzes the full impact of AI implementation for all stakeholders involved – from customers to suppliers to employees, including society as a whole. Simply put, it prevents misuse of AI for malicious purposes.

Why is ethical AI important?

AI is a powerful technology designed and developed to replicate, augment, and in some circumstances replace human intelligence. The technology frequently involves developing insights from big data originating from multiple sources. Poorly designed AI projects built on faulty, inadequate, or biased data can result in unintended and potentially harmful consequences or risks, including:

Potential risks involved in AI projects

Risk category

Risks

Performance
  • Errors
  • Bias and discrimination
  • Imitating possible prejudices from real-world data and preferences
  • Opaqueness
  • Lack of interpretability
  • Lack of stability in performance

Economic
  • Wage increases or squeezes
  • Job displacements
  • Income distribution
  • Economic Inequality
  • Centralized power within one or a few enterprises

Security
  • Cybersecurity
  • Data privacy

Control
  • Lack of human agency 
  • Clear accountability

Organizational
  • Financial performance
  • Organizational reputation
  • Legal and compliance
  • Discrimination

Societal
  • Misinformation impacting human-to-human interactions
  • Manipulations
  • Surveillance and warfare
  • Intelligence divide

Ethical
  • Explainability
  • Responsibility
  • Fairness
  • Misuse

Ethical AI is the cornerstone upon which loyalty and trust are built by developing a framework that establishes guidelines for AI’s responsible use and implementation, mitigating the unintended consequences. Ethical AI promotes a system of moral tenets and approaches to use AI responsibly, factoring in social, economic, and ethical implications.

Best practices for responsible and ethical AI

Enterprises building and developing AI products need a plan to mitigate the ethical risks and avoid pitfalls. Similar to other risk management strategies, a customized, operationalized, scalable, and sustainable ethics program governing organizational data and AI implementation must systematically perform risk assessment throughout the organization. Best practices include

  1. Institute a governance body that reviews data privacy, cyber security, compliance, and other data-related issues. An AI ethics strategy must fit together easily within the general data and AI strategy devised at the executive level.
  2. Create a robust ethical AI framework that involves articulating ethical standards for the organization, external and internal stakeholders, and a governance structure outlining how this structure is maintained and managed. A good framework is tailor-made to industries and establishes KPIs and a quality assurance program that measures the continued effectiveness of the business strategies.
  3. Make governance outputs explainable and accurate by implementing an ethical AI framework that ensures high-level and granular guidance, including providing process support via customized and optimized tools for managers.
  4. Develop a cultural and organizational-level awareness of data and AI ethics and related strategies that involve educating and upskilling employees to raise critical concerns to the appropriate deliberative body. Ensure that the organization understands why data and AI ethics matter throughout all processes.
  5. Perform qualitative and quantitative research to monitor impacts and engage stakeholders to ensure that AI products are ethically developed and deployed.
  6. Monitor the impacts of AI and the associated data produced. Keep relevant stakeholders educated and informed on what a product or process should and should not do.

Best practices in ethical and responsible AI are governed by AI principles known as Asilomar AI Principles. These 23 guidelines outline and mandate measures that guard against unethical or unintended AI system outcomes. These principles are broadly divided into three categories

Asilomar AI Principles
Research
    • Research goals – AI research should be to create valuable intelligence.
  • Research funding – Investments and funding in AI research should be to ensure its beneficial use.
  • Science-policy link – Facilitates a healthy and constructive relationship between AI researchers and policy-makers.
  • Research culture – Promotes transparency, trust, and cooperation between AI researchers and developers.
  • Race avoidance – AI teams avoid cutting corners on safety standards.
Ethics and values
  • Safety – Ensure safe and secure AI systems throughout their operational lifetime.
  • Failure transparency – Developers of failed AI systems should be sure about the reasons for their failure and share lessons learned.
  • Judicial transparency – Any involvement of an autonomous system in judicial decision-making should provide a satisfactory explanation to a competent human authority.
  • Responsibility – The designers and developers of AI systems should take responsibility for their use, misuse, and actions 
  • Value alignment – The goals and behaviors of AI systems must align with those of human values throughout their operation.
  • Human values – AI systems operate in parallel and alignment with human dignity values, rights, freedom, and cultural diversity norms and mores.
  • Personal privacy – Privacy should be respected with appropriate rights given to access, manage, and control the data generated.
  • Liberty and privacy – AI systems should not curtail people’s liberty unreasonably.
  • Shared benefit – AI should be used and applied to benefit and empower people.
  • Shared prosperity – The economic benefits earned from AI should be shared to benefit humanity as a whole.
  • Human control – Humans should control the AI systems, specifically in choosing how and whether to delegate decisions to AI systems.
  • Non-subversion – Highly autonomous AI systems should not subvert and instead should respect and improve society’s social and civic processes.
  • AI arms race – A race in lethal weapons should be avoided.  
Long-term issues
  • Capability caution – Avoid strong assumptions on future AI capabilities regarding its upper limits.
  • Importance – AI needs to be planned and managed with care.
  • Risks – Any risks from AI systems must be subject to preplanned mitigation efforts.
  • Recursive self-improvement – AI systems that can recursively self-improve to enhance quality or quantity are subject to safety and control measures. 
  • Common good – Intelligence gathered from AI should be widely and broadly shared for a positive societal benefit.

 Recommended practices to address ethical questions in AI

Leading technology companies like Google* have focused on ethics in AI using a targeted approach that fits systems to purpose. These companies suggest building an ethical AI strategy that embraces all stakeholders and involves

  • – Using a human-centric design approach that offers a good user experience, provides clarity and control, and augments and achieves precision.
  • – Early feedback in the design process followed by live testing before moving to full deployment.
  • – Identifying multiple metrics to monitor and assess training and understand the tradeoffs between errors and experiences at a granular level.
  • – Understanding the limitations of a dataset and communicating the same to users wherever possible.
  • – Conducting rigorous tests to ensure that the AI system is working as intended and can be trusted.
  • – Monitoring and updating the system after deployment. Consider both long-term and short-term solutions for any issues that arise.

*Note: Google recommends general practices such as these that ensure fairness, interpretability, privacy, and security for ethical and responsible AI systems.

At an accelerated pace, AI is transforming the world and how we interact with technology and each other. The potential of AI is tremendous and will continue to lead us to bold discoveries that will shape human history. In this journey, we must understand the need to act responsibly and ethically as good corporate citizens and ensure that this fantastic technology shapes the brightest future possible.

If you are interested in incorporating AI into your business processes ethically and responsibly, send your query to intellect2@intellect2.ai. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect2TM. IntellectDataTM develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellect2.ai.

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