The challenges of building a responsible, practical AI model
Artificial intelligence can help companies solve complex business challenges and identify new opportunities — but businesses need to be able to trust the technology. Currently, only 25 percent of businesses have processes fully enabled by AI and only 20 percent of companies have an ethical AI framework. This can create serious business consequences.
 
In today’s business environment, however, AI is vital. Retailers, for example, are facing a hectic holiday season and AI can create better online experiences at speed. AI increases productivity, product quality, and consumption. By automating tedious tasks, AI helps companies alleviate the burdens facing IT teams that are holding down the fort during these unprecedented times.
 
Despite all the benefits that come with innovation, teams need a responsible AI framework in place and a toolkit before they start using AI. AI as a technology is neutral — it is not inherently ethical or unethical. Instead, AI is a system that conforms to the norms and standards of society. It is critical to evaluate what controls, requirements or standards are or should be in place to achieve this.
 
However, adopting Responsible AI is also a big challenge for businesses and organizations. It is usually mentioned that the use of Responsible AI is incompatible with the business. We will discuss:
 

What is Responsible AI?

Responsible AI is a framework or practice of building AI solutions having clear and transparent rules on how it uses data, processes it, and generates insights from both an ethical and legal point of view.
 
 
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Why is Responsible AI important?

The impact of an AI initiative that a business rolls out may have a far-reaching impact and not just from a technology or performance upgrade perspective. There could be perceived changes in the way how the societal or cultural traits of a demographic base of consumers react to the initiative. Netflix and Spotify redefining movie and music experiences for the masses are examples. Both brands have tons of AI material in their core digital fabric and leverage it from time to time to surprise customers with more personalized engagements.
 
Similarly, the COVID 19 pandemic saw the widespread deployment of AI-powered chatbots. Nearly all consumer-facing businesses used them to answer the flood of queries coming in from customers in all sorts of areas while their physical business support divisions had to work with limited capacity due to COVID regulations. These traits are likely to continue and evolve into more man-machine interactions.
 
As the future moves into an economic model where machines play a greater role in society, it is important to ensure that ethics and legal credibility are an integral part of every AI initiative within a business. They mustn’t perceive it as an add-on feature that can be integrated at the discretion of developers.
 
The talk around this AI framework is not just in the corporate boardrooms. Governments around the world are finding ways to promote the use of ethical standards in technology development in high specialization areas like artificial intelligence and machine learning. The European Union has already released guidelines for ethical AI development and implementation in 2019 and other countries like the United States, India, Japan, and China are following a close pursuit.
 
 
 
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Responsible AI adoption Challenges

Some key challenges that need to address for the successful adoption of AI:
  1. Explainability and Transparency: If AI systems are opaque and not able to explain themselves as to why or how specific results are generated, this lack of transparency and explainability will threaten Trust in the system.
  2. Personal and Public Safety: The use of autonomous systems such as self-driving cars on roads and robots could be a risk of harm to humans. How can we assure human safety?
  3. Automation and Human Control: If AI systems can generate Trust and support humans in tasks and offloads their work. There will be a risk of threatening our knowledge related to those skills. This will make it more complex to check the reliability, correctness, and result of these systems as well as makes human intervention impossible. How do we ensure human control of AI systems?
  4. Bias and Discrimination: Even if AI-based systems work neutrally, they will give insights into whatever data it is trained for. Therefore, it can be affected by human and cognitive bias and incomplete training data sets. How can we make sure that the use of AI systems does not discriminate in unintended ways?
  5. Accountability and Regulation: With the increase of AI-driven systems in almost every industry, expectations around responsibility and liability will also increase. Who will be responsible for the use and misuse of AI systems?
  6. Security and Privacy: AI systems have to access vast amounts of data to identify patterns and predict results that are beyond human capabilities. Here, there is a risk that the privacy of people could be breached. How do we ensure that the data we are using to train AI models are secure?
 

How companies can overcome these challenges in adopting responsible AI?

This question will be discussed and answered in our upcoming Global AI Webinar where top AI scientists from Google will reveal their secret to making AI adoption real.
 
Register now to save your spot