Important Ethics in AI
Artificial intelligence (AI) technology empowers machines to perform tasks with human intelligence. AI, machine learning (ML), and big data analytics are among the top technologies that have revolutionized business operations across industries. AI has become such an essential part of our lives that it is no longer an emerging but a widely embraced technology that is evolving at a very fast rate.
With a keen interest in AI advancements, more people are undertaking the artificial intelligence course in Chennai and all over the globe. These are the very creators of the autonomous systems that are being developed with the hope of taking away responsibilities off human shoulders to allow them to spend precious time innovating rather than performing cumbersome, repetitive tasks.
AI-powered systems have been hailed to work faster, round the clock, and with minimal error compared to their human counterparts. Thus, they have been adopted widely to boost productivity and efficiency in business operations. As we rely more heavily on AI, a worthy concern is that sooner or later, AI’s capability will match or even surpass that of humans. Yet questions arise on the ethics and moral standards of AI systems to replace humans. What are the implications of our heavy reliance on AI systems on humanity?
Valid concerns about the implementation of AI technology
AI is the next big solution to many challenges in operations and human lives, but its implementation begs the question of how it can be implemented without losing humanity to its adoption. There is widespread fear of AI taking over jobs meant for humans and rendering them jobless or AI being biased in many forms when applied to processes such as recruitment. We break down some of the greatest concerns that stem from the advancement and adoption of AI technology.
1. Machine bias
On one side, some have imagined that intelligent machines will eliminate biases associated with humans to promote utmost transparency. These biases include gender bias, racial bias, recruitment processes bias, creditworthiness assessment bias, racial bias in predicting criminal activities in urban areas, sexual bias, bias based on skin tone, and social biases associated with residence and socioeconomic status. However, machine biases stem from biases in data, machine programming, and machine process outcomes. Experts in the field believe that bias in systems can be reduced but not completely eliminated. Secondly, the goal should be to explain the biases that exist in systems in a way that is humanly understandable and acceptable.
2. The problem of opacity
Opacity refers to the lack of explainability of decisions made by algorithms in autonomous systems. Opacity refers to a lack of transparency in AI systems whereby users of a system or an algorithm are unable to understand the inner workings of the system theoretically, i.e., the computations that take place inside an algorithm up to the time that they deliver an outcome. Only the creators of the algorithm understand its workings.
This may be caused by:
- The workings of the algorithms and their self-learning nature are too complex for the users’ cognitive capabilities.
- Lack of visualization tools with the capacity to dissect the code and operations of the complex algorithms
- Data or code that is poorly structured
- Deliberate action by corporations, government, and data brokers
This presents a disadvantage to system users since there is no way they can tell how reliably a system is processing or using their data.
3. Privacy
In an era where cyber security is a top concern, privacy during the use of personal data is protected to prevent unauthorized use or misuse. The ethical issue when collecting and using data in AI is the source of training data. Not all individuals consent to their data being used as training data or in whichever way, even though we assume so. Are there regulations and policies to safeguard personal data from unauthorized transfer to third parties?
4. Morality and legality
With smart machines taking on more serious responsibilities like decision-making, concerns arise about their moral and legal status to do so.
What level of control do humans lose in the process?
What are the long-term implications of machines making decisions on the capacity of human beings to do the same?
These are the right questions to ask since AI is technically replacing human beings on many tasks and decisions e.g., stock-trading, driving, spelling, and others. It is harder for humans to be in control, especially where AI has to make quick decisions for instance in the case of autonomous cars.
The importance of ethics in AI
As data continues to grow and be used to draw insights for important decision-making, AI is also evolving at a very fast rate as the world relies more and more on technology. This means that the time to start developing human-centric AI is now more than ever. It is of utmost importance to understand AI technology and its shortcomings so far before it becomes too complex in the future, yet it plays such a critical role in the life of human beings.
Why is ethics important in AI?
1. It allows for the formulation of AI development guidelines and ethical standards
Implementing ethics in AI allows for the creation of safety guidelines and ethical standards that evolve alongside AI advancements to protect the interest of humanity and the environment, thus paving the way for responsible AI that builds and compliments human existence rather than threatens and damages it.
Even though AI has simplified life, it carries the potential of having a negative emotional and psychological impact on humans. For instance, the anxiety and uncertainty of knowing that sooner or later, an intelligence machine may render one jobless. This is because AI focuses more on measurable outcomes than on human rights and morals. Ethical AI promotes a healthy coexistence between humans and AI so that AI is developed only with the aim of promoting human autonomy and decision-making. Its starts at the point of studying the social impact of AI.
3. Prevent unfair bias
Bias is one of the greatest challenges of implementing AI technology. Sadly, the reliance on AI systems is overwhelming, given that people believe AI systems to be neutral. However, bias in AI stems from the developers, complexity of AI algorithms, and lack of visualization tools to interrogate AI decisions and understand biases. If ethics is taken into consideration at the point of development, unfair bias against groups or individuals may well be minimized and transparency of prediction algorithms be realized.
4. Data privacy and security
Ethical AI prioritizes data privacy and security through the implementation of data principles, governance, and best practices as well as proper model management systems. Proper governance shields humanity from unintended repercussions that come with AI-powered decisions. Also, AI solutions should be designed in a way that they prevent or promptly detect data leaks, data misuse, or unauthorized access. At their best, AI systems should maintain data quality and integrity.
5. Promotes accountability
This is among the core principles of AI. AI systems should be transparent and accountable for the decisions and outcomes computed by AI models throughout the life of the system.
In conclusion
AI has evolved and is more essential to humans and business operations than ever. AI intelligence will soon match or surpass human intelligence, and there is no denying that the world has greatly benefited from AI. The role of AI ethics is to ensure that we do not lose control of the very useful technology that we created to increase productivity and efficiency. The ethical obligation of AI creators is to integrate and evolve ethics alongside the evolving technology. This demands that we understand the workings of AI, build upon its shortcomings, and then drive its advancement to continuously support humanity and the environment without threatening them. Finally, ethical AI also involves regulating the creators of AI.