Intro
In recent years, the discussion of "data ethics" and "AI ethics" has shifted from the realm of nonprofits and academics to top tech companies like Microsoft, Facebook, Twitter, and Google. Even mainstream media outlets have joined the conversation. These companies and consumers have recognized the critical need to address ethical problems associated with the vast collection, analysis, and use of data, particularly when it comes to training AI models.
Why are these companies investing in data and AI ethics? Simply put, failing to prioritize these issues can have severe consequences. As AI becomes more accessible to the general public, companies need to make their use of AI as transparent and morally sound as possible. Beyond potential damage to their reputation and legal risks, failing to operationalize data and AI ethics can result in wasted resources, inefficiencies in product development, and even an inability to effectively train AI models.
Currently, many companies approach data and AI ethics in an ad-hoc manner, dealing with issues on a product-by-product basis. Without a clear protocol for identifying, evaluating, and mitigating risks, these companies may overlook potential concerns or hastily respond to problems as they arise. Some may even hope the issues will resolve themselves. When attempts have been made to address the problem on a larger scale, overly broad and imprecise policies often result in false positives and hinder production. This challenge is magnified when third-party vendors, who may not prioritize ethical considerations, are involved.
To mitigate the risks involved, companies need a practical, clear, and operationalized approach to data and AI ethics. Like other risk-management strategies, this approach must comprehensively identify ethical risks throughout the organization, from IT and HR to marketing and product development. An effective AI ethics strategy will be accessible to every employee and potential consumer. By prioritizing data and AI ethics, companies can navigate the ethical risks associated with data use and AI development while ensuring responsible and sustainable practices.
Traditional Approaches to AI Ethics
When it comes to addressing ethical risks in data and AI, the traditional approaches are a good place to start. These methods for understanding AI ethics can be helpful, especially at the research stage. The ideal ethics guide is almost always a combination of the strategies in each approach.
The "On-the-Ground" Approach
Within businesses, engineers, data scientists, and product managers are eager to address risks relevant to their products. This approach is the practical application without as much of the conceptual focus of the academic approach.
High-Level AI Ethics Principles
Companies like Google and Microsoft have outlined their AI ethics principles but operationalizing them is challenging. Determining what abstract values like "fairness" mean in practice, in real-time, is a monumental task, with an endless number of definitions and metrics.
The best solution is a combination of these approaches paired with realistic expectations. Ideally, a company’s ethical AI guidelines should leverage the critical thinking of the academic approach, the practical knowledge of the on-the-ground approach, and the scale of the high-level approach.
How to Make it Work
In today's diverse and dynamic business landscape, one thing is clear - AI ethics cannot be a one-size-fits-all solution. Instead, it requires a tailored approach that addresses the specific needs and regulations of each company. Here, we present six essential steps to help you develop a data and AI ethics program that is customized, operationalized, scalable, and built to last.
Step 1: Leverage Existing Infrastructure
The key to success lies in utilizing the power of your existing infrastructure. This can seem like a daunting task these days, when AI governance is constantly evolving, both in the United States and on an international scale. However, getting a good grasp on what already exists is crucial to establishing consistency. Tap into resources like data governance boards, which already discuss key risks such as privacy, cyber threats, and compliance. By involving the voices from the frontlines, you can identify and address concerns effectively. Additionally, gaining buy-in from executives is crucial, as it sets the tone for the organization's attitude towards ethical issues and ensures alignment with overall data and AI strategies.
Step 2: Establish an Ethics Council or Committee
If your company lacks a dedicated ethics body, consider creating one. This council or committee should include experts in areas such as cyber, risk and compliance, privacy, and analytics. Including external subject matter experts, such as ethicists, can bring diverse perspectives and enhance the effectiveness of the program. A variety of organizations have developed guidelines of their own that can be a tremendously helpful starting point for any company. UNESCO, for example, posits four core values that AI should be held up against:
- Prioritize human rights and human dignity
- Promote peaceful, fair, and interconnected societies
- Ensure diversity and inclusivity
- Foster the well-being of ecosystems and the environment at large
Step 3: Develop a Tailored Ethical Risk Framework
Craft a robust framework that aligns with the unique characteristics of your industry. This framework should define the ethical standards and risks specific to your company and identify key stakeholders. Establish a governance structure and outline how it will adapt to changing circumstances. It's crucial to set KPIs and implement a quality assurance program to ensure ongoing effectiveness.
While your company can find inspiration in any successful application of an ethical framework, it’s crucial to tailor your guide to the specific needs and risks of your industry. For instance, the ethical concerns of a financial institution will be significantly different from the concerns of a consumer retail operation. Many experts, including the Harvard Business Review, recommend taking cues from the healthcare industry, which has been especially invested in building robust, ethical frameworks for at least the last 50 years.
Step 4: Integrate Ethical Risk Mitigation into Operations
Your framework must go beyond theory and provide practical guidelines. Define ethical standards for all involved parties, from data collectors to product developers and managers. Establish a clear process for escalating ethical concerns to senior leadership or an ethics committee. Ensure processes are in place to detect and prevent biased algorithms, privacy breaches, and unexplainable outputs.
Every company, no matter its industry, benefits from a trustworthy reputation. This trust is best maintained through transparency and precautionary measures. It’s always best to be proactive rather than reactive when it comes to ethical issues. Integrating AI risk mitigation in every department of an organization will ensure consistency across the board.
Step 5: Continuously Measure and Improve
Regularly assess the effectiveness of your program by setting standards and key performance indicators (KPI) and implementing a quality assurance program. Continuously refine your tactics to align with evolving circumstances and personnel changes. Seek out specialists on matters of AI governance and ethics and invest in their expertise. Companies like Fairo.ai offer products and services that are dedicated to keeping up with the changing regulations surrounding AI and providing best-in-class tools, services, and integrations to ensure AI is consumed successfully and ethically.
Step 6: Foster a Deep-Rooted Culture of Ethical Responsibility
Creating a culture where a data and AI ethics strategy can be successfully deployed and maintained requires educating employees and empowering them to raise key concerns at crucial junctures. Employees who interact with data or AI products, whether in HR, marketing, or operations, should understand the company's data and AI ethics framework. It's important to clearly articulate why data and AI ethics matter to the organization, demonstrating that the commitment is engrained in everything the organization does. Ultimately, building a successful data and AI ethics program requires a holistic company culture built upon core belief in upholding ethical principles.
Proportionality and Do No Harm
AI systems should be utilized strictly within the bounds of necessity to fulfill legitimate objectives. Employing risk assessment is crucial for mitigating potential harm arising from these applications.
Safety and Security
AI actors should take measures to avoid and address both safety risks (unwanted harms) and security risks (vulnerabilities to attack).
Right to Data Privacy
Privacy must be safeguarded and encouraged throughout the entire AI lifecycle. It’s crucial to establish appropriate frameworks for data protection.
Stakeholder Diversity and Collaborative Governance
Data usage should adhere to both international law and national sovereignty. Moreover, inclusive approaches to AI governance require the involvement of diverse stakeholders since AI affects a hugely diverse population.
Responsibility and Accountability
Auditing and tracing AI systems is necessary to ensure compliance with human rights and environmental well-being. Effective oversight, impact assessment, and due diligence mechanisms should be implemented to prevent conflicts.
Transparency and Explainability
The ethical implementation of AI systems relies on their transparency and explainability. The degree of transparency and explainability should be suitable for the given context, as conflicts may arise between transparency and explainability and other principles like privacy, safety, and security.
Sustainability
Assessing AI technologies should consider their effects on 'sustainability', which encompasses a dynamic range of objectives, depending on the associated context. AI’s potential should not outweigh sustainability efforts in any given arena.
Awareness and Literacy
Promoting public awareness of AI and data can be achieved through accessible education, active civic participation, developing digital skills, providing AI ethics training, and fostering media and information literacy.
Fairness and Non-Discrimination
AI actors ought to advance social justice, fairness, and non-discrimination by adopting an inclusive approach, thereby ensuring that the benefits of AI are accessible to everyone. AI models are trained on data that is produced by humans and humans are prone to bias. Wherever possible, AI actors should endeavor to not reproduce this bias.
Final Takeaway
Implementing the principles and strategies outlined in this guide will keep your organization on the leading edge of the intersection between business and AI. The practical application of a human-centric approach to AI ethics that prioritizes fairness, transparency, and security can seem daunting. Fairo is designed to help you navigate these complexities. In addition to providing solutions in many of the categories detailed above, our system sits on top of your existing infrastructure and ecosystem. We provide a window of observability and expertise into all your systems as they relate to responsible AI consumption, development, and deployment.
How Can Fairo Help?
Fairo is a SaaS platform focused on standards, simplicity, and governance to give organizations and their users the confidence to consume AI successfully and rapidly at scale. Fairo is committed to being the industry-standard platform for helping your organization implement its AI governance framework and strategy. Fairo seamlessly integrates into your existing ecosystem and is easy to use. AI is a disruptive technology that will change how people work and live. We envision a world where AI is universally built responsibly, trusted, and not feared. We aim to provide an easy-to-use solution that helps organizations procure, develop, and deploy trustworthy AI solutions with confidence.
