Artificial intelligence has now been integrated into almost every part of our everyday lives. From the financial sector to even medical it has a very wide spectrum of use. However, with its advantages, there is also a negative side to AI use. AI has been used for several unethical purposes, and one of the prime examples is Deepfake.
A survey back in 2019 showed that 42% of employees have experienced ethical issues while using AI globally. Hence, in this article, we are going to discuss what ethical AI is and its examples and principles.
Artificial intelligence in the 21st century has become an inseparable part of people’s lives, with issues of data protection, biases in algorithms, and over-transparency emerging as crucial ones. The first call for specifically AI ethics was made by the Future of Life Institute in 2014. The modern definition of ethical AI includes principles like equality, responsibility, and transparency to make AI useful for society and non-hazardous.
What is Ethical AI?
Ethical artificial intelligence is establishing whether an artificial intelligence system is functioning in a proper ethical manner and adheres to human rights. There is a set of concepts and recommendations based on which organizations work on AI development, implementation, and regulation. In its simplest form, Ethical AI aims at doing no (more) harm, reducing or removing bias, and increasing representation privacy and security.
Ethical AI should also involve cooperation with ethicists, policymakers, and technologists, as well as the public because AI governance should be based on an integrated approach. This also translates to efforts to make sure that the AI systems are built to reflect society’s better and acceptable standards and more can be relied upon to make decisions that have impacts on individuals’ lives.
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Principles of Ethical AI
Some of the important principles of Ethical AI are:
- Fairness: AI systems should operate fairly so that none of the individuals or groups of people is advantaged or disadvantaged. This comprises the collection of diversified data, a periodic review of the system for bias, and avoiding unfair consequences on particular groups of people.
- Transparency: AI systems’ activities and actions should be comprehensible and their rationale clear. This comprises explaining how an algorithm or data source was generated and why certain results are obtained and explaining why an AI made a certain decision about how users’ data is used and by whom.
- Accountability: Guiding developers and operators of AI systems to account for their conduct. This includes coming up with clear policies on who bears the responsibility in situations where AI inflicts harm on others.
- Privacy and Security: AI systems have to guard against user information privacy and the use of the data inappropriately. This means it is necessary to implement strong security measures to protect the data from being exposed.
- Inclusivity: AI development ought to be an interdisciplinary affair that requires the input of people with multidimensional diversity in terms of culture, social status, and profession. This is to make sure that there are equitable solutions to be applied by AI systems to all sorts of people in society so that in the course of development, any form of prejudice and ethical dilemmas are noted.
- Human-Centric Design: The design of AI systems should be such that they augment human endeavors and benefit human beings.
- Sustainability: AI systems do have an impact on the environment and this should also be taken into account. This pertains to the energy efficiency of AI algorithms and hardware, environmentally friendly materials and processes in the development of artificial hardware, and other social implications where AI can become a game changer.
- Ethical Governance: Implementing guidelines and standards for the use of Artificial Intelligence. This involves establishing best practices that should govern the development of AI, establishing and empowering bodies that will be responsible for ensuring the rational use of AI and promoting or punishing against the proper use of AI.
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How does Ethical AI work
Ethical AI is process-based, where principles and best practices for what AI should be and how it should be built are put into practice. The following are the aspects as to how Ethical AI works:
- Fairness: This is about making sure that the AI systems that are being developed and used in decision-making processes do not have bias. For instance, AI employment tools must provide merit-based assessments of individuals and should not be racially or sexually biased. Developers do this by varying the training data and constantly checking prejudices.
- Transparency: From here, it can be seen that the urgent and fundamental task is the transparency of AI systems. For instance, when a financial AI system is used to approve a loan, it should be in a position to give reasons for the approval. This kind of transparency assists the users in knowing how their data is being processed and the use of a basis on which the algorithm is arriving at decisions, which is very important in building trust and ensuring accountability.
- Accountability: Reviewing the legal regulation of AI, the author explains the need to develop measures to check the developers and users of AI. For example, in healthcare, the system that is used for diagnostics of diseases is required to be tested and approved by the authorities. There should be a protocol and way adopted in case the whole system of the AI is wrong in diagnosing an ailment.
- Privacy and Security: Guarding the user information against piracy and other forms of misuse.
The below table discusses the key practices and objectives, in short:
| Key Practices | Objective |
| Fairness | Maintain Equity and impartiality |
| Transparency | To have a clear understanding of how it works |
| Accountability | It can be held accountable in case of any issue |
| Privacy and Security | Prevention of misuse and safeguarding of user information |
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Examples of Ethical AI
- Healthcare Diagnostics: AI systems like IBM Watson assist doctors by providing data-driven insights for disease diagnosis, ensuring accuracy and reducing human error. These systems are regularly updated and audited to ensure they operate fairly and transparently.
- Educational Tools: They also incorporate AI whereby a platform such as Coursera will try to read students’ habits and performance to recommend what to give them next as well as how the content should be presented. All these tools are meant to be used with equal facilities for all the children who are regarded as equal in a classroom.
Step-by-Step Process of Designing an Ethical AI System
There are several steps as to how you can design an Ethical AI System. The following are the steps to design an ethical AI system:
Understanding the Scope of AI Use
This is the first step where you should understand for what purpose the AI is being used in the system and what is the scope of it. In this stage, you will be able to identify the risks and potential dangers if it has any. This initial step covers all the basic checks as to whether the AI can have any malicious or unethical scopes while being deployed for use.
Involving stakeholders
This is one of the most important steps in implementing Ethical AI. When stakeholders are involved in the AI systems, they will automatically raise questions and this leads to more transparency about how the AI systems are being used. The more transparency, the more there is a chance that the AI system is being used ethically as stakeholders won’t risk investing in platforms that have the potential to be corrupt and thus will build confidence and trust.
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Implementing Ethical Principles
The third stage is to implement the principles discussed above like fairness, transparency, accountability, sustainability, ethical governance, etc. This will ensure that the AI system is reliable and honest and stakeholders as well as people who are going to use it after being deployed can have faith in the concerned AI.
Deployment
Once all the above 3 steps are completed, the final step is to deploy the AI system, and thus an Ethical AI is implemented.
Conclusion
Ethical AI is a concept that can be defined as the process of creating only positive AI systems and using them only for good purposes. According to the principles of justice, equality, publicity, and participation, the developers & users of AI can create systems that are reliable and secure in terms of human rights. Hence, with the development of AI, it is also necessary to implement ethical motives to make it beneficial for the future of our society and not cause any harm.
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