Depending on who you ask, AI will either save us all or be the crumbling downfall of humanity. Most likely, it will have elements of both and still turn out far different than anyone predicted.
Humanity has made great progress over millennia; we have turned science fiction into reality and brought advancements that have helped millions lead happier, healthier lives. We have devices that connect us to the information and world around us, medicine to extend our lives, and, for some reason, a selfie toaster that will burn an image of your face on your morning toast.
We truly are masters of innovation.
Our history, however, is also stained by greed, incompetence, and sometimes (looking at you, toaster) just truly bad ideas. AI will likely have moments of immense innovation and good, as well as harmful consequences. While we might not be able to predict in which areas we will fail or succeed, there are steps we can take to hopefully mitigate some of these risks.
While humanity has made some truly grave errors over the years, there is an underlying severity and potential permanence around AI that makes missteps immensely more frightening or impactful. Getting this right, or at least not totally screwing it up, will require communal effort from a wide range of players. Countries, institutions, and corporations will all need to practice AI governance that encourages and enables the safe development and deployment of AI. The frameworks, policies, and processes will shape how these tools are developed and if we prioritize technologies that enhance human life, or be the footnote in history books of when we really started to mess things up.
Ethical Considerations in AI Governance
Defining and abiding by ethical AI governance is complex. How do you predict and mitigate every potential problem that might arise from a technology that we barely understand and that changes expeditiously? How do we not only avoid existential risk but also ensure that the technologies we release into the world are fair, equitable, and for the betterment of humanity?
One place to start is with the data used to train the models on which these AIs are built. As the popular adage goes, garbage in, garbage out. Datasets are crucial in developing AI models because they serve as the foundational input that enables these models to learn and make accurate predictions later in the real world. High-quality, representative datasets ensure that AI systems can generalize and adapt to real-world scenarios. Conversely, biased or incomplete datasets can lead to skewed results and reinforce existing prejudices, undermining the fairness and trustworthiness of AI applications. Careful curation, preprocessing, and continuous updating of datasets are essential to maintain the integrity and performance of AI models.
Datasets are messy and imperfect because we as humans, are messy and imperfect. Most data has been collected over the last few years as our devices, apps, cars, and even smart refridgerators have been sucking up anything and everything they could learn about us. In many ways, this data represents the ‘best we could do’ at the moment, but nearly every dataset collected has issues with incomplete or inaccurate data, imbalances, or is simply not specific enough for the problem at hand.
There is also immense growing concern over privacy. Until recently, most people had no idea how much information devices were collecting and what that data was then used for. Even now, the general public likely does not understand the full scope of the issue. Privacy and consent are important no matter the industry, but these issues become even more critical when it comes to sensitive information like health data.
Historical biases have also seeped into our datasets, recycling and reviving some of humanity's worst convictions. Whether it’s sentencing recommendation algorithms that disproportionately suggest longer sentences or reduced access to bail for African American defendants, or an AI hiring tool that learned to not recommend women for top positions because historically, there haven’t been many women in top management positions, our AIs are mirroring some of our least flattering traits. In both of these cases and many more, these AIs were seen as magic bullets that would improve policing or hiring and dissolve the exact human biases they later found to have amplified.
Our imperfect world will never generate perfect data. So, what does that leave us with? Do we shrug in exasperation, saying if we can’t achieve perfection, we shouldn’t try? Of course not.
Improving Data Quality through Generative AI
In areas where we cannot trust data to be unbiased, there are privacy concerns, or the kinds of data we need simply do not exist, synthetic data might be a way to evade some of these issues.
Synthetic data, as its name suggests, is data that is not based on any actual data or event. It is created by algorithms and simulations that aim to mimic real-world data. It can be used to train machine learning models, validate mathematical models, and test software systems under diverse and controlled conditions without the need for sensitive or proprietary real-world data. Synthetic data can also be generated in large quantities, providing a virtually limitless supply of training data. This is especially useful in situations where real-world data is scarce, expensive, or difficult to obtain. Using synthetic data also mitigates privacy concerns and compliance issues related to data sharing and usage. Since synthetic data does not contain real personal information, it is less likely to violate privacy regulations such as GDPR or HIPAA.
While there are good reasons to believe that synthetic data might be a better alternative, it does not come without its risks and downsides. First, synthetic data may not perfectly capture the complexities and nuances of real-world data. This can lead to models that perform well on synthetic data but poorly in real-world situations because of the differences in data distributions. In its aim for perfection, synthetic data can also be too perfect and overfit patterns and characteristics that exist only in the synthetic data. Generating high-quality synthetic data can also be expensive and require specialized expertise. It is also very resource-heavy in an industry already scrutinized for using as many resources as some small countries.
Synthetic data might alleviate some of the concerns around safety and compliance, but it is only one of the hydra's many heads.
Human-in-the-Loop Systems
It is debatable if our AI systems will ever be able to truly run autonomously, away from humanity's watchful eye and scrutiny. Keeping human judgment as a condition for any AI decision-making tool is another vital step in improving AI safety and governance. Human-in-the-loop (HITL) systems ensure that human judgment and intervention are maintained in critical AI applications, preventing unintended consequences that could arise from fully autonomous decisions.
HITL systems are an important safeguard because they combine the strengths of human intuition and machine precision. Especially in high-stakes scenarios such as healthcare, law enforcement, and autonomous vehicles, having a human oversee the AI's decisions can be the difference between life and death. For instance, an AI system diagnosing medical conditions can quickly analyze vast amounts of data, making it an excellent tool for tagging and identifying potential abnormalities. A human doctor, however, can provide the context, experience, and empathy that the AI might lack. Through their training and experience, they also might be able to identify common errors or abnormalities the AI has not yet been exposed to.
The presence of a human in the loop adds a layer of accountability, ensuring someone can question AI's decisions, provide feedback, and make the final call. This is crucial for decisions significantly impacting someone's life, as human oversight can catch and correct errors that AI might miss or misinterpret. Human intuition and understanding help identify when something doesn’t look right or provide context that AI lacks. Moreover, having humans in the loop to correct mistakes aids the AI in learning and improving its outcomes over time.
Effective HITL systems do not happen automatically though. In order for these systems to be effective, they require well-designed interfaces that allow humans to interact effectively with AI, understand its decisions, and intervene when necessary. Training is also essential to ensure that the humans involved are equipped to manage and oversee AI systems proficiently. Part of this training also needs to include information on just how reliable (or not) these systems actually are. In the ProPublica report that found sentencing recommendation systems to be racially biased, reporters spoke with numerous judges who said they generally go along with the AI without much consideration. There are complex reasons for this, but a major one is simply that many people trust AI systems over their own judgment. There is a (likely undeserved) trust that many place in the output of AI systems simply because they assume the model knows best. In order to be truly effective, humans not only have to be in the loop but also aware of the limitations of these models.
Ensuring Compliance in AI Applications
While improving datasets and keeping humans in the loop for important decision-making will do a lot to help improve the AI systems we develop, there is still a strong need for government regulation to create the norms and safeguards required for safe development. While each country has its own approach to AI legislation, general principles of compliance, including transparency, accountability, fairness, and privacy, must all be a part of the governance process.
One of the primary challenges in ensuring compliance is the rapid pace of AI development. Technologies and methodologies evolve faster than regulatory frameworks can adapt, creating a gap between innovation and regulation. To address this, organizations can adopt a proactive approach by integrating ethical considerations and compliance checks throughout the AI development lifecycle. This includes conducting impact assessments, engaging with stakeholders, and building flexible systems that can be updated as new regulations emerge.
Another challenge is the global nature of AI, which means that compliance must be managed across multiple jurisdictions with differing laws, standards, and abilities. As we look to the future, the challenges of ethical AI governance will only grow more complex. Innovations will continue to push the boundaries of what is possible, making it imperative for stakeholders to work together in creating and maintaining robust governance structures. By prioritizing ethical considerations, improving data quality, and ensuring compliance, we can guide AI development in a direction that benefits humanity, minimizes risks, and upholds our values. The journey ahead is harrowing, but with collaborative effort and steadfast commitment, we can navigate the path toward a future where AI serves as a true force of good for all of humanity.