It is often quoted that with great power comes great responsibility. The story is still relevant, especially since AI is taking over the world. ChatGPT has risen to the limelight and opened the door to new possibilities. Bard is also following this bandwagon.However, artificial intelligence can end up being a double-edged sword. Thus, enforcing specific regulations and frameworks concerning AI-centric solutions is essential.
Best AI Safety Measures for Developers & Organizations
Google and other independent bodies have outlined certain principles for developing AI applications. These guidelines aim to aid in the development of responsible technology. Furthermore, there is an explicit mention of things or objectives that are off the limits. Let us take a closer look at the best AI measures to prevent AI misuse after product development.
Principles to prevent AI misuse in software development lifecycle:
Here is list of most recommend practices during your product development.
1. Inculcate Sustainable Privacy Design
Considering Sustainable Privacy Design while developing AI applications is of utmost importance. In simpler terms, privacy measures should be robust and sustain any situation. Typically, developers can use architectures with privacy safeguard mechanisms. The developed solutions should be centred around privacy and transparency. Most importantly, the users need to have absolute control over the use of their data.
2. Ensure that Society as a whole benefits from AI
Unfortunately, few realise that artificial intelligence can benefit humanity like never before. Developers are duty-bound to ensure the new technology helps society as a whole. The AI-powered solutions could impact sectors like entertainment, transportation, governance, security, energy, media, and more.
Google suggests carrying out a risk-benefit analysis. It helps in deciding how technology impacts humanity. We need to keep in mind that the world is a diverse place. Thus it is important to respect regional norms, societal norms, and other rules in the country of operation. Lastly, you can decide on the subscription model.
3. Hire and groom a team of Data Ethinicists
I can list several technologies that ended up doing more harm than good. In most cases, the companies/ developer didn't have ill intent. This is where data ethicists come into the picture. They are the people whose judgement on ethical codes is trusted and endorsed by many. Data Ethicists carry out an analysis and help eliminate the moral dilemma.
Data Ethinicists have a bird-eyes view of your project. They can swoop in and correct things while developing. This saves a lot of money and resources in the long term.
4. Stay away from intentional or unintentional bias
Bias is an integral part of the human mindset. Sometimes it can be intentional, and most of the time, unintentional. AI algorithms are, after all, trained by a human. It is not uncommon to induce unfair bias. It can adversely affect race, nationality, income, ethnicity, and political and religious beliefs. Thus it is important to neutralise such biases.
The best way to do this is via an external audit. There is a good chance the external team will spot biases.
5. Adhere to high standards
One should strive for excellence. The new technology has to be a benchmark. AI tools are extremely capable of opening new doors in scientific research. This is especially true in biology, chemistry, medicine, and environmental sciences. Stakeholders have to aspire to higher standards and relentlessly promote best practices.
6. Appoint an AI Review Board
No matter what we do, accountability is of prime importance. Typically developers and creators are very close to their work. Sometimes they fail to see the downsides of their creations. Appointing an AI review board comes with multiple benefits. Firstly, the review board can analyse technology from an external perspective. Furthermore, they are in a better position to understand how the underlying technology can affect individuals or society as a whole.
If needed, the board will suggest design changes and coding AI technologies. The bottom line is that the AI review board helps better the product and enhances accountability.
7. Maintain an AI Audit Trail
We have already talked about the importance of the AI Review Board. More often than, people working on the project change. Changes and suggestions mentioned in the audit might not be evident to the new team members. Thus an AI Audit Trail must be maintained.
Team members can check the AI Audit Trial to understand biases and other significant issues. They need to ensure that such cases are not repeated.
8. Establish a platform for moderation and remediation
Important papers and technologies are often subjected to peer review. The reason is to improve the quality of the papers and add credibility. Use tools to create a comment section. Moderators can react to the comments. Meanwhile, creators can consider suggestions or changes and incorporate the same.
The remediation part comes into play if the AI technology has caused harm to individuals, organisations or a particular set of people. Set up systems that tell you how to react. In extreme changes, the developers may need to change the feature or fine-tune the technology.
9. Consider the "Human problem."
Unfortunately, not everything can be deduced by algorithms and data sheets. Humans often see AI as a threatening technology, and rightly so. AI is expected to supercharge effectiveness, productivity, and effectiveness. However, there is another side to this as well. Some people might lose their jobs, and many repetitive tasks no longer need human intervention. Companies and developers need to keep this in mind.
10. Accountable to the people
With the help of the above-mentioned mechanism, Ai technologies can be accountable to people. As a creator, one should be open to feedback. People should be able to appeal against AI technology via a readdress forum. Developers need to evaluate the concern without any biases. If required, forward it to the concerned department.
11. Test for safety and privacy
The world has witnessed a spate of cyberattacks in the recent past. Privacy and safety are significant factors. We have seen cases of how elections were allegedly swayed using botnets. AI can cause harm on a much larger scale in the wrong hands. The hacker could develop multiple AI models to disrupt an entire country.
It is much more efficient if necessary security and privacy features are built into the tool beforehand. More than often, AI tools have access to user data. Strong privacy measures required to ensure data safety. The user should have absolute control over their data. The data in any form can be shared only with the user's explicit consent.
12. Monitoring product community across social media
Most of the technological harms getting spread through social media very fast. So its important to monitor product user community to understand loopholes. This will help to identify issue and mitigate as soon as possible.
Introducing Responsible Artificial Intelligence Institute
AI is still in its developing stage. Mostly new technology always come with very high risk. And AI is no different. The EU AI act has proposed harsh fines and even prison time for systems that are not compliant. This is where Responsible AI comes into the picture.
Responsible AI is an organisation especially created for AI regulations. It helps companies understand regional AI acts. This way, companies can implement technology keeping while staying compliant. Each region has its own rules and regulations regarding AI. Thus it is better to let the experts handle things. You can quickly mitigate non-compliance risk by doing so.
The RAI is a non-profit organisation. They have many tools and AI experts working relentlessly towards sustainable solutions. By joining the organisation, you can get independent assessments based on responsible AI benchmarks & help to prevent misuse of AI.
Search gaint Google also considering some important principles for AI Safety using their own AI safety at GOOGLE
AI is arguably the most disruptive technology in the market. It has opened doors for unprecedented levels of automation. On the flip side, artificial intelligence comes associated with equally high risks. The guidelines outlined in this article help establish a responsible benchmark for AI tools. So its very much important to include AI Safety practices into day to day development of any type of AI products. Its going to be new norm into product development lifecycle to avoid AI misuse.