Why AI Accountability Is the Need of Hour?
Published September 25,2018 1 year ago Posted By Justinas DanisReading Time: 3 minutes
Artificial intelligence is the machine ability to think and it is quite essential that it thinks right. From automatic cars to algorithms that sentence criminals, it must be ensured that AI can clarify and is accountable for the decision it makes, just like a chauffeur, judge, or any other decision-maker.
For app development in Lithuania or any location, keep reading, to know about AI and why its accountabilities are a must as, AI is finding immense inclusion in various apps.
But, lack of explain ability of decisions by Artificial Intelligence (AI) is a critical problem. And this inability also is a reason why AI is not deployed in areas like law, healthcare, and industries that deal with sensitive customer data. To understand how data are handled and how AI attained the point of decision making; regulations for data protection, mainly GDPR, have been reached. GDPR can grossly penalize companies/businesses who fail to give an explanation and record of how certain decision is taken either by machine or man.
IBM has made a big step by announcing a software service to watch and find biases in AI models and keep a track of the entire process of decision-making. This service is enabled to permit businesses/ companies to track AI decisions on the occurrence and supervise for any actions of biases to make sure that AI methods are followed as per regulation and follow the entire business goals.
How to avoid machine/data errors?
Jesus Mantas, the Managing Partner at IBM Global Business Services, said that explain ability was their main focus of research and the software is the outcome. The software has an ability to theoretically find out whether an algorithm is biased and decide the cause of biases.
This would allow businesses/companies to prove compliance with regulations for data protection by keeping a track on how an AI program makes use of data, and ensure that there is no compromise of sensitive results by a biased model.
Jesus Mantas says that if a GDPR complaint goes to court, would be able to give information on how and when a decision was taken, the factors responsible and if an algorithm was retrained’, in fact, every information as per regulations.
Data bias versus model bias
The cloud-based software runs simultaneously along with the common AI programs, like Watson, SparkML, Tensorflow, AWS SageMaker, and AzureML, so that AI decisions can be checked and taken in the right way. This mode of detection is different from a bias-detection software, because it monitors the model and the running data in real-time runtime, instead of looking after millions of data to understand and find out a bias.
Data bias is a real issue, as it was witnessed in some US courtrooms, which got advice from an algorithm that wrongly assumed that black people gets re-offend than white people. It was found that the algorithm was shaped with biased data, and hence the biased decisions.
A step to boost AI adoption
Besides this algorithm service, IBM released an ‘AI Fairness 360 Toolkit’, which happens to be a library of algorithms and is an open sourced.
As per a recent IBM report, though the willingness of AI adoption is quite high; fears on liability and a lack of technical skills are holding it back.
With this software, IBM can enhance its goal or purpose to improve global reach and collaboration of AI technologies, to understand the processes and aide the enterprises learn how to deploy AI in their company.
What can be perceived now, is that with time it will be clear if the software emerges in practice as its promises as the deepest algorithms of machine learning are quite difficult to interpret, but this can be taken as a huge step towards getting accountable AI programs which comes with explanations for action, and can assure the businesses and companies that, AI is worth the trust.
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