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Artificial intelligence has never been as easily accessible as it is today. The emergence of new technologies like generative AI and large language models has further increased its accessibility and ease of use by enabling a wide range of users to test the capabilities of AI. This has pushed the boundaries of content creation, information retrieval, and business intelligence.
However, this endless stream of innovation also comes with a unique set of drawbacks. For instance, there are several ethical dilemmas and data privacy concerns prompting organizations to seek a more ethical approach to AI implementation.
That’s where AI governance comes into play. When properly applied, it can be a powerful tool for enabling innovation while mitigating some of the risks associated with artificial intelligence.
In this guide, we discuss AI governance in its entirety, evaluating everything from what it is, its importance, and the various perspectives on AI governance framework that help guide policy ideas and implementation.
AI governance is a collection of policies, frameworks, and best practices that serve as guardrails to ensure artificial intelligence technologies are developed and utilized in a way that minimizes potential risk from bias while maximizing intended benefits.
It establishes the frameworks, standards, and rules that direct AI research, development, and application in a bid to ensure fairness, safety, and respect for human rights.
This includes addressing all oversight mechanisms associated with mitigating risks like privacy infringement, bias, and misuse. From an ethical perspective, AI governance requires the involvement of a wide range of stakeholders, including AI developers, policymakers, ethicists, and users. This way, it is much easier to ensure that all AI-related systems are developed and utilized in a way that aligns with societal values.
Although AI governance places some emphasis on the technical aspects of AI development, it is mostly centered on addressing flaws arising from human involvement in the creation of AI.
The reasoning behind this approach is pretty straightforward – since AI is a product of extensively engineered code and machine learning, which are all created by people, it is susceptible to human bias and errors. This notion also applies to generative AI models, which rely on extensive, open-source datasets for training.
In a bid to curb some of these issues, AI governance provides a structured approach to dealing with each challenge, ensuring that all machine learning algorithms and training data are monitored, evaluated, and updated to prevent harmful decision-making by AI applications.
Read more: Gen AI and Data Security: Safeguarding Sensitive Information
The use of AI in various sectors has had a profound impact on compliance. For instance, as policymakers become more aware of the significance of responsibility and accountability in developing and utilizing AI models, they are putting new policies in place to protect users’ right to privacy, mitigate risks, and encourage ethical usage. [1]
AI has also enjoyed significant integration into organizational and governmental institutions. Such high-profile utilization also means that any misstep could have drastic consequences. Take the Tay Chabot incident, for instance. Microsoft Tay, an AI-powered chatbot released in 2016 learned toxic behavior from social media and public interactions, causing the company to take it down within 24 hours. [2]
Similarly, COMPAS, a crime recidivism risk-assessment algorithm designed to assess the likelihood of a defendant’s recidivism, was found to be less accurate than random human untrained evaluators. [3]
Judging from these examples, it is clear to see that without proper oversight, AI can cause significant ethical and social harm. However, by providing frameworks and guidelines, AI governance aims to balance safety with technological innovation, ensuring that AI systems do not violate human rights or dignity.
In that regard, transparent decision-making and explainability have become vital elements of any AI deployment strategy. Essentially, it is important to understand how AI applications make decisions and hold them accountable for their decisions to make sure they make each decision fairly and ethically.
Moreover, AI governance isn’t about providing a one-off compliance approach – it is also about promoting ethical standards over time. As such, current governance frameworks are focused on more than legal compliance. They’re equally focused on AI’s social responsibility, thereby safeguarding against legal, financial, and reputational damage, all the while promoting the responsible growth of AI technology.
Read more: Generative AI Strategy Is a Must-Have: How to Build It
AI technology is evolving at a phenomenal rate. This is especially notable with the emergence of generative AI, which is capable of creating new content and solutions, giving it vast potential use cases across various domains. However, its broad applicability also necessitates the need for robust AI governance.
Robust AI governance is expressed in the form of well-thought-out principles that guide organizations in the ethical development and utilization of AI applications. These principles include:
Organizations must rigorously evaluate their training data to prevent real-world biases from getting embedded into machine learning algorithms. Ultimately, this ensures fair and unbiased decisions.
Artificial intelligence, despite the strides it has taken over the past two decades, does not have consciousness and thus is incapable of empathy. [4]This means that any organization looking to leverage AI capabilities must understand its potential societal implications – not just focusing on the technological and financial aspects.
Therefore, when developing AI models, it is advisable to anticipate and address any potential societal impacts of the technology and advise all stakeholders on the best way to mitigate and address these risks.
There is a great need for clarity and openness on how AI applications operate and make decisions. This means that organizations must be willing and ready to explain the logic and reasoning behind AI-driven outcomes.
The journey towards responsible AI governance doesn’t end at transparency – organizations should also proactively set and adhere to high accountability standards to manage any potential changes AI may bring about and maintain responsibility for the technology’s impact.
In 2023, the US government issued an executive order to ensure AI safety and security[5]. The executive order included a comprehensive strategy with frameworks geared towards establishing new standards to manage the potential risks of AI technology. Some of the most notable safety and security standards stipulated in the AI governance framework include:
AI safety and security governance frameworks require all developers of powerful AI systems to conduct safety tests and other critical data with the US government. This also includes the development of tools, standards, and tests to ensure AI systems are not only safe but also trustworthy.
According to a recent report by Statista, only 56% of consumers believe that retailers can ensure data protection when setting up generative AI tools. [6]The new directives by the US government are aimed at developing privacy-preserving techniques in both the research and development phases of AI technology. The framework also provides guidelines for federal agencies to evaluate the effectiveness of privacy-protecting techniques.
These guidelines are aimed at advancing the development of responsible AI governance principles in healthcare and education as well as promoting the development of life-saving drugs and AI-powered education tools.
Read more: Data Preparation for AI Initiatives: The Essential Steps
While AI carries vast potential in helping less experienced workers advance their creativity, it also carries the risk of limiting employment possibilities for workers it may eventually replace. [7]Worker support frameworks are geared towards developing principles to mitigate the harmful effects of AI on jobs and the workplace in general. Currently, this primarily involves addressing job displacement and workplace equality.
These frameworks are designed to ensure the government’s responsible deployment of AI models. They include guidelines for government agencies’ utilization of AI, improvement in the procurement of AI systems, and accelerated hiring of IT professionals.
As AI systems become more complex and engrained in critical processes, any organization looking to leverage the technology’s vast potential safely and ethically must balance innovation and accountability in all operations. This is essential for understanding and managing the outcomes of AI decisions and building trust among users.
Some of the most critical areas to consider when deploying AI systems include:
AI models are only as good as the data they’re trained on. They learn patterns and act accordingly. While this is an effective approach to learning and boosts accuracy, it can also lead to some unintended consequences.
Consider the case where Bing’s chatbots went rogue, confessing love to a user, even going as far as telling him to end his marriage. [8]While this may sound inconsequential, the same notion may cause harm in more serious situations.
The chatbot’s response was most likely a result of learning behavior and mimicking online conversations. That’s why human oversight is crucial in responsible AI governance. While some of the initial stages of AI development may be automated, there should be a human involved at every stage of the AI’s lifecycle.
The role of humans in responsible governance is monitoring the system’s operations and assisting in the design lifecycle to ensure the system generates accurate and reliable responses. The level of human oversight required comes down to the purpose of the system and the safety and control measures applied.
The utilization and societal impact of any AI application depend on how humans implement it. Therefore, it is the responsibility of humans to determine the appropriate role of AI in society and to take responsibility for its impact.
By implementing a proper AI governance framework, organizations and governmental institutions are better able to take responsibility for AI actions and effectively apply accountability when there are unpleasant outcomes.
One of the core principles of ensuring ethical responsibility is data protection. To achieve this, organizations should consider implementing an integrated approach for AI systems that encompasses security measures, contingency planning, and privacy protocols.
This will help organizations effectively establish a viable strategy for the development and deployment of AI initiatives. The strategy applied should include a fail-safe plan to address potential data breaches caused by malfunctioning AI applications.
Establishing data access and usage policies and implementing the principle of least privilege can also go a long way in building public trust in AI.
Read more: Challenges of Implementing AI: How to Overcome Them
Gone are the days when advances in AI technologies were confined to the lab. AI has now become a real-world application technology and part of everyday life. While AI has the potential to deliver great benefits to various sectors, some of these benefits may not be realized without proper care and effort.
This responsibility falls to civic and government action. Civic-government collaboration can play a crucial role in clarifying the expectations of AI applications on a context-specific basis. This involves focusing on crucial areas such as:
To build people’s confidence and trust in the accuracy and appropriateness of AI predictions, AI developers must provide an explanation of why an AI system behaves in a certain way. They must also ensure there is accountability and grounds for contesting the system’s output.
This can be achieved by assembling a collection of best practices, and providing guidelines for hypothetical use cases so that industry leaders can balance the benefits of using AI against practical constraints.
Organizations and governmental institutions must take proactive measures to prevent both accidental and deliberate misuse of AI. That said, there are many challenges to ensuring the safety and security of AI systems. For instance, it is nearly impossible to predict all possible system behaviors, especially when AI systems are applied to problems that are difficult for humans to solve.
For the best results, organizations and government institutions must outline basic workflows and standards of documentation for specific application contexts. These workflows should also be sufficient to show due diligence in conducting safety checks. They should also establish safety certifications to show that a service has been assessed and passed specified tests for critical applications.
As AI technology continues to evolve and new risks emerge, it has become vital for all relevant parties to implement proper AI governance. A proper AI governance framework can not only mitigate AI risks but also improve the technology’s potential for improvement and innovation. It can also build user trust in AI systems, which can go a long way in boosting AI utilization across various sectors.
Organizations have applied self and co-regulatory approaches to inform current laws and perspectives. While this has been largely effective in curbing the misuse of AI, newer technologies like generative AI and advanced machine learning algorithms may require a more stringent approach that can only be achieved through civic and government collaboration.
References
[1] Int. comp. The rise of AI and its Impact on Compliance. URL: https://www.int-comp.org/insight/the-rise-of-ai-and-its-impact-on-compliance. Accessed on August 29, 2024
[2] Incidentaldatabase.ai. Incident 6. URL: https://incidentdatabase.ai/cite/6. Accessed on August 29, 2024
[3] Incidentdatabase.ai. Incident 40, URL. https://incidentdatabase.ai/cite/40/#r700. Accessed on August 29, 2024
[4] Theconversation.com. Empathetic AI has More to Do With Psychopathy than Emotional Intelligence. URL: https://tiny.pl/z325vk0j. Accessed on August 29, 2024
[5] Whitehouse.gov. President Biden Issues Executive Order on Safe Secure and Trustworthy AI. URL. https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence. Accessed on August 29, 2024
[6] Statista.com. Consumers Privacy Concerns About AI. URL: https://tiny.pl/cn5km0kk. Accesssedon August 29, 2024
[7]Imf. org. AI will Transform the Global Economy. Let’s Make Sure it Benefits Humanity. URL: https://tiny.pl/rx3q7syj. Accessed on August 29, 2024
[8] Economictimes.indiatimes.com. Chatbot Goes Rogue, Confesses Love For User. URL: https://tiny.pl/84cc01fj. Accessed on August 29, 2024
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