Council Post: Towards An Ethical Tech Revolution: Building Responsible AI Practices
For anything to thrive and sustain, ethics are a must; technology innovation is no exception! Businesses, in general, have incredible growth potential owing to recent AI advancements such as generative AI, but this also entails a lot of responsibility. Since technology directly affects people’s lives, there is a lot of emphasis on AI ethics, data governance, trust, and legality. Businesses must be aware of new and impending regulations as well as the procedures they must undertake to ensure their organizations are data-responsible and compliant when they begin to scale up their AI usage to reap business benefits. Responsible AI can help with this goal. So what exactly is ‘Responsible AI’? Responsible AI is the practice of designing, developing, and deploying AI with the purpose of having a fair influence on consumers and society, allowing businesses to build trust and confidently scale AI. This refers to a framework with predefined principles, ethics, and rules to govern AI. Responsible AI, also called ethical or trustworthy AI, ensures that any AI system operates according to moral principles, complies with rules and regulations, and reduces the risk of reputational and financial harm. As opposed to being an expensive burden or just a risk-avoidance mechanism, it actually is an enabler of technology. Businesses that adhere to these criteria are typically rewarded with more accurate AI models, less waste in their deployment, and, overall, more sustainable benefits. While implementing and deploying AI, one must abide by certain general principles. Additionally, it’s equally critical to note how these principles are put into practice in a manner that fosters the development of a responsible AI ecosystem. Framework of Responsible AI The proposed framework of Responsible AI has two facets: Conscientiousness – Thought level Solid Governance- Execution level Each pillar of Responsible AI should be dealt with conscientiousness and solid governance, ensuring adherence at the thought as well as execution level. Let’s look at the two facets in detail: Conscientiousness (Thought Level): The goals of artificial intelligence (AI) must be humanistic. The developers and users of AI must demonstrate responsibility at thought level showing a strict regard for doing the job well and thoroughly. The conscientious approach implies painstaking efforts to follow one’s conscience and an active moral sense governing all actions of an individual/institution as one implements each of the ten pillars of responsible AI listed in this article. Solid Governance (Execution Level): Even the best-designed model could produce undesirable and unanticipated behavior if there is weak governance. The architecture that oversees the design, creation, and use of a machine learning model is known as governance. All pillars of responsible AI should be under the jurisdiction of a solid governance system. A clear structure of administration and control must be in place, even though the specifics of effective governance vary from model to model based on the application and intended use. Key Pillars of Responsible AI: ●      Inclusiveness (Non-bias): The principle of non-discrimination ensures that a qualified person shouldn’t be denied an opportunity by AI systems solely because of their identity. In terms of education, employment, access to public areas, and other issues, it should not further the damaging historical and social divisions based on religion, race, caste, sex, descent, place of birth, or domicile. Additionally, it should try to prevent discrimination by identifying affected stakeholders, determining attributes for inclusion or exclusion, fostering the creation of a diverse AI workforce, and testing the AI model with diverse users. ●      Transparency: It implies the requirement to demonstrate and document the methods by which AI systems are developed along with their strengths and limitations. A simple and understandable example of AI is risk or fraud models. In a transparent scenario, you would have visibility around the source and features of training data as well as the development approach of the underlying algorithms along with their shortcomings. Long-term disaster avoidance, along with the realisation of AI’s potential for good, depend on open and transparent AI. ●      Explainability: The inner working of an AI model, along with its potential biases and expected effects, are all described in terms of explainable AI. When putting AI models into production, a business must first establish trust and confidence. Humans find it difficult to understand and retrace how an algorithm arrived at a result as AI advances. Explainability seeks to enable users to get explanations for decisions impacting them in simple and intuitive language. For example, explainable artificial intelligence can provide explainations relating to an AI system’s decision of selection or rejection of a resume. ●      Accountability: There should be a clear allocation of roles and responsibilities throughout the AI life cycle, along with identification of stakeholders accountable for outcomes of the AI system. This essentially calls for individuals/institutions to take responsibility for actions of AI systems and their consequences, for example, of results from ChatGPT or any credit risk model. Accountability entails making developers and vendors aware of adherence to responsible AI principles and compliance with existing standards and regulations. Robust control over AI processes, including humans in the loop, and timely feedback from all stakeholders and users aid accountability. ●      Privacy: Like other forms of technology, AI systems should be able to defend against threats and safeguard sensitive data. If privacy concerns are not considered, one could run the danger of being quickly identified and having their data compromised. Privacy entails getting user consent before storing and using personal data, safeguarding and avoiding repurposing of collected personal data, transparency of data access and usage, and implementing all necessary privacy controls. For example, in the healthcare sector, companies must do pre-work on data, such as anonymization and de-identification, when using patient data for AI purposes to comply with HIPAA regulations. ●      Security: To stop hackers from meddling with the system and altering its intended behavior, an AI system’s and its underlying data’s security is essential. It is critical to identify and mitigate system vulnerabilities – put in place strong access controls, secure coding practices, controls against data dwindling, model stealing, and malicious use, as well as ensure adequate security controls when dealing with third parties and open source components. Responsible AI can pave the way for security to be established by ensuring system robustness and security against misuse or adversarial attacks. ●      Reliability: Any trustworthy system must be dependable and secure. The same is true for AI systems. Since AI systems permeate the fundamental fabric of human experiences, they need to be dependable. But what does reliability in AI entail? To begin with, it ensures that the AI is reliable in varied situations, much like a trustworthy human who can provide rationally consistent responses to complex problems. In addition, the reliability of AI systems (e.g., for collections) is characterized by the selection of appropriate algorithms, reproducibility of outcomes, monitoring data or model drifts, presence of feedback loops, and quality assurance checks across the AI product lifecycle. ●      Safety: Public safety may be significantly impacted by vulnerabilities brought about by the growing usage of AI in crucial areas of society. Organizations must create AI systems that function well and have no or minimal adverse impact. The presence of human supervisory systems and decommissioning in case of system failures are critical to ensuring the safety of AI systems. What will happen if an AI system malfunctions? What actions will the algorithm take in an unexpected situation? If AI addresses every “what if” and reacts to the new circumstance effectively and without endangering users, then it can be said to be safe. For example, a self driving car killing pedestrians as it was not prepared to deal with people in the middle of the road is unsafe and needs to build in appropriate safety mechanisms. Practical and affordable grievance redressal system and compensation mechanisms should also be put in place. ●      Compliance: AI systems must comply with all applicable laws, statutory standards, rules, and regulations in all stages of their life cycles. Organizations must build awareness and constantly monitor the state of the AI regulatory environment locally and globally to ensure compliance and avoid reputational or financial losses. Organizations must take precautions to prevent data misuse and only use data with consent. Corporate-wide data and AI compliance, along with associated rules and practices, must be established by organizations. A recommended step is to start auditing, which comprises examining the data design, proposed model, and purpose. Compliance in AI should be proactive on the company’s part, not after the act. ●      Alignment with Human Values: The fundamental goal of AI should be the maximisation of human potential in alignment with human values. This entails a critical review of AI use cases, deep diving into anticipated benefits, harms, and overall impact on society. To safely accomplish human objectives and the values that underpin their realization, it is imperative that human values become integrated into or inseparable from the processes in which an AI system learns to make evaluative decisions. The “codes” we feed into AI algorithms should align with human objectives and values. Promise of Responsible AI AI has the potential to significantly impact all industries, including financial services, retail, manufacturing, healthcare, logistics and even space exploration. A responsible AI framework enables companies to track and mitigate bias and create transparent and explainable AI models, prevent misuse and adverse effects of AI, determine who to be held responsible if something goes wrong, and ensure compliance with security, privacy, and associated regulations. Prevention of misuse with appropriate usage guidelines and implementation of a continuous feedback loop can go a long way in maximizing positive returns from AI. Responsible AI must, however, overcome several challenges, including access to appropriate data sets, adopting the most suitable data infrastructure and dealing with the black box nature of complex algorithms. Organizations should focus on creating or adopting responsible AI toolkits comprising frameworks, KPIs, best practices, assessments, checklists and relevant technologies. If used properly, artificial intelligence could revolutionize the game by positively transforming the world and improving the quality of life. While a conscientious approach is crucial to the development of responsible AI, to direct AI in the future, there must be solid governance, suitable legislation, and regulation. This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.
A responsible AI framework enables companies to track and mitigate bias and create transparent and explainable AI models, prevent misuse and adverse effects of AI, determine who to be held responsible if something goes wrong, and ensure compliance with security, privacy, and associated regulations.
A responsible AI framework enables companies to track and mitigate bias and create transparent and explainable AI models, prevent misuse and adverse effects of AI, determine who to be held responsible if something goes wrong, and ensure compliance with security, privacy, and associated regulations.
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Swati Jain is a seasoned professional with over two decades of analytics and consulting experience across multiple verticals, including financial services, retail, media, logistics, and healthcare. Currently, she is the Vice President, Decision Analytics at EXL Service. She leads a world-class team of data scientists and AI/ML professionals impacting marketing, risk management, and operations of several Fortune 500 organisations. She has been instrumental in developing multiple award-winning innovative AI solutions along the customer journey, propagating newer business models and democratising AI in client organisations.