Jyoti Nigania

Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree. ...

Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.

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How Biasness Can Be Reduced in AI- Powered Chatbot

Jun 8, 2018 | 720 Views

Chatbots simulates human conversation, through artificial intelligence. It helps in communicating with a real person. Chatbots have gained maximum popularity and now they become talk of the town. Chatbots are made to ease the pain that the industries are facing today. The purpose of chatbots is to support and scale business teams in their relations with customers.

Hence, Artificial Intelligence chatbots using Natural Language Processing (NLP) are increasing at the fastest pace in all the industries. Today, Chatbots are replacing live chat and other forms of slower contact methods such as emails and phone calls. They operate every day throughout the year without a break. This improves the customer experience and helps the organization achieve desired outputs. Chatbots respond instantly and this instant response helps in maintaining the image and brand of a company. Humans are able to communicate one at a time, whilst chat bots can simultaneously have conversations with thousands of users. With chatbots, every query is answered. Having a chatbot eliminates minor problems and caters to each and every person ensuring that every order is successfully operated.

Bots and artificial intelligence solutions help a lot to humans with thousands of tasks in all the industries. Social bias is increasingly significant conversation in the artificial intelligence community. Artificial intelligence can learn only when as much as the examples can be exposed to it and if the data is biased then automatically the machine will also be biased. 
So, according to the experience of training of many researchers with IBM Watson, they suggested that we can minimize bias in AI applications by considering following points:

Thoughtful about the data strategy: AI architecture have choice to train their AI chatbot as if the data example available to them are not proper representative of the population it may cause error or provide inaccurate results. AI architecture can go for choices to create diverse data examples either from data scraps available or real examples but one need to be very careful in using data set examples to trained their AI-Chatbot. The biggest challenge is what will they do if the real examples are not representing the population accurately. To overcome this, one can opt for laissez faire approach, the benefit is that you can optimize performance to the general population of users which may possible at the expense of underrepresented population that we don't want to ignore.
For example, if the most population interacting with chatbot are under the age of 65 and we are not training them for the questions related to old age issue, chatbot will not be able to answer the questions that the user above 65 age seeks. For this the company has developed synthetic training questions and used other data source to prepare osteoporosis screenings and fall prevention counseling related question. Thus, the chatbot should learn a wider range of topics without unfair preference for the interests of all users.

Encourage a representative set of users: We have partial control over who interacts with a chatbot or an AI system, but we can ensure that it will be accessible to all users by eliminating all the barriers which hinder the equal use by all the users. According to the survey, majority of chatbot users were under the age of 65. So for customization we could also add design options like font size that would make it easier for older people to use the tool. Similarly we can find many other ways to modify the content, the user experience so that the basic feature of chatbot can be served to all.  

However, with a diverse set of users, companies may run the risk of introducing a bot. To minimize this risk, it is essential to allow a wide range of perspectives in the design process and this leads to the final suggestion.

Create a diverse development team: If experienced and diverse team informs your decision-making, then there is less likely to introduce new biases into the system. But different members in development teams prove to be a challenge, especially in the AI field. It's very necessary to create a situation that inspires growth and empowers all the team members participate in the development. In our team there should be the mixture of all kind of people both from technical or non-technical background. All members should have the readiness to learn from each other, and find that each of our prospects brings something unique to our robot. This is the truth that sharing knowledge is the fastest path to success, both for the product and for the people creating it. 
AI is a field in which everyone can easily work and understand it. Like when we are designing an artificially intelligent system, we are often making very human choices. If our bot learns by example and designed by diversifying team members, then we are responsible for setting a good example.

Hence, artificial intelligence powered chatbots open the doors of endless business opportunities providing the compelling writing and the visuals. They can effectively deal with every aspect of branding while offering a competitive edge to entrepreneurs and leads to higher productivity.

Source: HOB
Jyoti Nigania

Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree. ...

Full Bio 

Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.

Internet of Things (IoT) Beyond the Hype
today

A Guide to Career Path towards Blockchain Technology
today

ZARA: Leveraging AI, Big Data and Analytics
today

Robotics in Automation: Transforming the Business Process
yesterday

Data Psychology: The Future of Data Scientists
yesterday

Scope of AI and Machine learning in India
24621 views

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12600 views

AI Race 2018: Top 10 Indian Startups
5907 views

Big Data and Data Science Job Opportunities In 2018
5352 views

Chatbot: The Next Big Thing
4965 views