New Approaches to Mitigating AI’s Negative Impact
AI is a tremendous and terrible tool, with the potential for significant positive and negative effects. Getting the most out of this revolutionary and powerful technology requires mitigating the negative impacts while maximizing its benefits. By continually looking for new approaches to maintain the delicate balance of artificial intelligence’s positive and negative effects, the world can use AI for the good of everyone.
The Positive and Negative Effects of Artificial Intelligence
The positive effects of artificial intelligence include:
- Human augmentation: AI can offer operational analytics that helps human workers in their tasks, recommend content appropriate to each work situation, and otherwise act as a person’s helping hand. Employees can work smarter rather than harder when equipped with these resources. Their work quality also increases, and AI can help in skills and professional development.
- Improved work efficiency: AI is excellent at automating many repetitive tasks that are boring for human workers. With those duties offloaded to AI systems, workers can focus their attention on higher-value tasks. Instead of spending all their time on paperwork or other administrative tasks outside of their job description, they can focus on their core competencies.
- Getting value out of massive data sets: Organizations have a massive well of data sources available to them, but this information is useless if they can’t analyze it at scale. AI fulfills this role and surfaces actionable insights in many key areas.
- Reducing human errors:Time-consuming tasks can lead to disengagement and boredom. If a worker lacks focus, they can make mistakes. AI can reduce the potential for human errors and also acts as another approval check for workflows.
- Improving decision-making:Stakeholders can use AI recommendations to guide their decision-making. Predictive analytics, for example, can show potential outcomes for a scenario and recommend the best course of action.
The negative effects of artificial intelligence include:
- Biased AI algorithms:AI bias can be introduced in several ways. One of the most common issues comes from a lack of diversity in the team programming AI algorithms. The limited perspective can lead to unintentional discrimination and other issues. Bias in the data sets is another way for this issue to occur. The data set may fail to collect important information or draw the wrong conclusions based on what’s available.
- Work automation eliminating jobs:The drawback of widespread automation is that it can drastically reduce available positions or even eliminate many jobs. While many people use a typical example of an automated fast-food cook, many office jobs are also at risk. Societal shifts in how work is perceived and the provision of social safety nets may become necessary as AI matures.
- AI-powered weapons:Automated weapons without human oversight is another negative impact of this technology, coming right out of the pages of dystopian science fiction novels. The potential of mass destruction is vast in this frightening use case.
- Malicious AI usage:Malicious actors can put AI to use for their illegal activities, such as hacking. These AI-powered solutions can lead to rapidly evolving cyber threats that can have widespread consequences.
- Unintended AI logic:Sometimes, the worst negative impacts are entirely unintentional. AI solutions may create “logical” decisions that make sense to the system but not in a way their developers intended.
Understanding Artificial Intelligence and its Positive and Negative Effects
AI has a vast potential for good, transforming entire industries with its capabilities. While the negative impacts of this technology are significant, there are ways to mitigate these issues without hindering the development of beneficial solutions.
Staying on top of the negative effects keeps the world away from potentially catastrophic consequences, and active mitigation is needed to manage the cultural changes that AI is introducing. Without these controls in place, widespread poverty, vast inequality, and even war could result.
Approaches for Addressing AI’s Negative Effects
By being aware of AI’s challenges and putting active mitigation plans in place, organizations can make the most of this powerful technology. Stanford University is adopting a new mitigation approach by recognizing and addressing potential harm early in the development process.
The main reason for taking this approach is because negative impacts can go unrecognized during the development process. Once the AI solution is live, the harmful effects get discovered after the launch. While some industries have ethics review processing built-in, such as healthcare, the IT industry lacks an emphasis on this. A researcher or developer may not consider the ethical implications of what they’re creating, which can cause lasting harm on an individual or societal level.
Stanford University’s mitigation method involves AI researchers reviewing their proposals to see whether the potential for societal and ethical harm exists. The researchers needed to explore artificial intelligence’s positive and negative effects. If the researchers find any problems, they need a concrete plan on how to minimize those negative effects. In some cases, they also need to work with an interdisciplinary faculty panel to handle the problems thoroughly. The AI researchers’ funding is contingent on going through this process.
This review panel also helps researchers identify areas of concern that they may not have picked up on. The outside perspective is helpful in thinking outside of the box to think about the potential for the AI to go wrong. By considering all angles of a particular solution, the AI implementation is built around an ethics-centric framework from start to finish.
Adapting to Changes in AI Technology
AI is a continually evolving technology, and its ethical considerations must be kept in mind as it changes. Organizations must consider how the changing capabilities of AI can result in negative impacts that are not present in earlier versions of the solution.
The review process should take place frequently throughout development and after the system goes into production. While finding and fixing negative effects before a system goes live is ideal, it’s not always possible to think of everything. Reacting quickly once new problems surface is just as important as the rest of the process.