AI Readiness: a Threat to Ethics in Technology Sector

by August 20, 2020

Artificial Intelligence

Artificial Intelligence (AI) is bringing new technology and changes to the world every day. Thanks to ‘AI Readiness.’ People are willing to take technological changes as they adapt to a society where intelligence and robotics do most of the job. But the problem here is ethics.

While talking about artificial intelligence and AI Readiness, very less is taken into consideration on the ethical part. Ethical implications and setting up the mechanism for compliance is a must in opening the world to a high-tech position without trouble.

 

What is AI Readiness?

The initiative by organisations to the next stage or to ongoing success depending on having the right elements in place across skills and resources, infrastructure and technology, process and module is AI readiness. The organisations could be at various stages in the AI journey. To pursue and generate real value for AI initiative, organisations must have a degree of AI readiness.

 

Organisational readiness: Companies that prefer to work seriously on AI and bring development should have an appropriate organisational structure, leadership and talent among employees. The leadership in the AI sector should be strong with talented analytics-specialized role players. Vision on why, how and what to work and strategy to plan for making the vision reality are the mandatory readiness. Stakeholders of the company should show their willingness to sponsor and support the initiative.

 

Technological readiness: The organisation requires equipped infrastructure and technologies like cloud-based tools and services, analytics tools and algorithms, effective software development processes and methodologies to comply with the AI readiness. Technologies are functional only when humans set their hands at work. So, technological improvement needs people to work on it. Another vital part is played by data readiness which includes its quantity, depth, balance, representativeness, completeness and cleanliness.

 

Financial Readiness: AI could be expensive at times. Therefore, all the inventiveness requires a budget on money, labour and technological usage. It is not an easy task to acquire all these in a company that has multiple departments working in different initiatives. The AI development group should have a grip on their project, have an insight into its outcome and talk effectively to acquire financial and physical support.

 

Cultural readiness: To promote scientific innovation and disruption, the organisation should have culture, mindset and a set of processes. Earlier, people used to make decisions based on their individual perception and gut feeling. But this doesn’t work anymore. The decision one takes on AI-related queries should incorporate data into account.

AI readiness plays a vital role in the development with its wide-angle coverage on all the AI-related extensions. But it neglects to follow ‘Ethics’ throughout the process. In a study conducted by Capgemini Consulting on Artificial Intelligence Benchmark, it is revealed that the term ‘ethics’ appears only once in 30 pages. Ethics should be embedded with the working system as it is often ignored in the AI development process. Ethics and trustworthy AI requires more than just a written or printed script in the paper. It needs to be followed practically with training and monitoring in the AI works.

There are various ethical risks involved when the organisation is not sticking to ethical conduct. In general, ethics refer to standards of morally good or bad, right or wrong initiatives. When humans are given control over the robotics and AI, the possibility of them imposing their strategy on race, gender, inequality to the system is high. This is a societal threat as it could end with robots and AI technologies becoming discriminatory high-powered humans.

Detecting unethical behaviour or processes with AI requires keen attention on numerous social contexts like discrimination, diversity and inclusion, gender equality, improper use of health records, invasion of privacy, lack of sustainability, missing governance, conflicts of interest, insensibility and inequality. If an AI system works without considering any of this, then it might face serious problems.

A major setback to break the routine mistakes could be by learning the risks of using prohibited and high-risk data. By sensitizing the damage control ways to the organisation and learning the well known-failures, the wrong steps could be avoided in further use.

 

AI should follow data ethics

Data ethics are a regulatory measure to current and historic frameworks, cultural mores and physical organizations. The most important part to understand when it comes to ethical issues involving data is the combination of personal information and ownership of data. The personal information of a person’s data file could even feature the individual’s qualification, living strategy, people and institutions they interact with.

The use of such personal information involves a lot of issues if it fails to cope with ethics. Therefore, ethical applications should follow simple guidelines that could be useful for formulating a more detailed policy.

• Technical experts need to sensitize policymakers regarding ethical threats.

• AI researchers involved in AI technology development should acknowledge that their works could be used maliciously if it doesn’t follow ethical guidelines.

• The protection gears and techniques should be brought into use with the help of cybersecurity experts.

• Ethical framework for AI should be developed and followed in accordance with the individual or the technology involved.

• Discussion based on the ethical framework of AI should involve AI scientists, policymakers, businesses operating in the sector and the general public.

• Identify data risks associated with the dataset, data collection and sourcing, data types, data utilisation and correct in as quickly as possible.

• AI employees should learn the documentation techniques to guarantee reproducibility, interpretability and peer review.

AI might look like a simple thing when people speak or read about it. However, working with AI and making use of it in everyday life involves a lot of process and strategies. AI mandates good infrastructure, skills and data to progress. What is left quite often is AI ethics. If AI takes initiative work with the ethics to protect human data privacy, then it could lead to a secure AI future.