Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps. While black-boxing AI systems can make the user experience seamless, hiding the seams risks disempowering users to mitigate fallouts from AI mistakes. While Explainable AI (XAI) has predominantly tackled algorithmic opaqueness, we propose that seamful design can foster Humancentered XAI by strategically revealing sociotechnical and infrastructural mismatches. We introduce the notion of Seamful XAI by (1) conceptually transferring "seams" to the AI context and (2) developing a design process that helps stakeholders design with seams, thereby augmenting explainability and user agency. We explore this process with 43 AI practitioners and users, using a scenario-based co-design activity informed by real-world use cases. We share empirical insights, implications, and critical reflections on how this process can help practitioners anticipate and craft seams in AI, how seamfulness can improve explainability, empower end-users, and facilitate Responsible AI.

This article is part of a series where we walk step by step in solving fintech problems with Machine Learning using "All lending club loan data". In previous articles, we prepared a dataset and built a Logistic Regression model, and we discussed the most common "ML model evaluation metrics" for a classification problem in the fintech space. This article will try to "understand" how our model decision works and what packages can help us to answer this question. Machine learning models are frequently named "black boxes". They produce highly accurate predictions.

Artificial Intelligence and its inherent bias seems to be an ongoing contributing factor in slowing minorities home loan approvals. An investigation by The Markup found lenders were more likely to deny home loans to people of color than to white people with similar financial characteristics. Specifically, 80% of Black applicants are more likely to be rejected, along with 40% of Latino applicants, and 70% of Native American applicants are likely to be denied. How detrimental is the secret bias hidden in mortgage algorithms? It's important to note that 45% of the country's largest mortgage lenders now offer online or app-based loan origination, as FinTech looks to play a major role in reducing bias in the home lending market, CultureBanx reported.

Artificial Intelligence and its inherent bias seems to be an ongoing contributing factor in slowing minorities home loan approvals. An investigation by The Markup found lenders were more likely to deny home loans to people of color than to white people with similar financial characteristics. Specifically, 80% of Black applicants are more likely to be rejected, along with 40% of Latino applicants, and 70% of Native American applicants are likely to be denied. How detrimental is the secret bias hidden in mortgage algorithms? It's important to note that 45% of the country's largest mortgage lenders now offer online or app-based loan origination, as FinTech looks to play a major role in reducing bias in the home lending market, CultureBanx reported.

Traditional finance is infamous for its slow and underwhelming processes. Loans used to take weeks of queuing, filling, and waiting. Investing and portfolio management used to take a dozen people to manage. But finance is taking a turn for the better. An OpenText survey of finance professionals cited by Business Insider reports that around 75% of big financial institutions use AI to strengthen their banking capabilities.

Specialty auto lenders are unheralded champions of the economy -- they make auto loans available to people with below average credit who would otherwise be unable to buy a car. These cars help people get to work, travel and elevate themselves in life, driving short-term and long-term economic benefits. Specialty auto lenders face heavy regulatory scrutiny, significant capital requirements, high business cyclicality and difficult relationships with the media, which is often quick to label these businesses as predatory. The industry's challenges have been frequently highlighted in news coverage, including articles from Bloomberg, Business Insider and the Wall Street Journal. Despite the challenges, specialty auto lenders have an imperative to approve as many creditworthy applicants as possible.

This is not a blog about Old MacDonald or his farm. Instead it is about Artificial Intelligence (AI) in the mortgage industry. And we will NOT allow any sarcastic, caustic or offhand remarks about the mortgage industry needing some kind of intelligence. First of all, exactly what is artificial intelligence, at least how it is described of late? One thing it is not is fake intelligence (not related to fake news … and you might like this site that helps YOU create your own fake news … but I digress, and so soon ... sorry).

Business process automation is a booming multi-billion-dollar industry that promises to remove menial tasks from workers' plates -- through the introduction of autonomous agents -- and free up their time and brain power for more creative and engaging tasks. However, an essential component to the successful deployment of such autonomous agents is the ability of business users to monitor their performance and customize their execution. A simple and user-friendly interface with a low learning curve is necessary to increase the adoption of such agents in banking, insurance, retail and other domains. As a result, proactive chatbots will play a crucial role in the business automation space. Not only can they respond to users' queries and perform actions on their behalf but also initiate communication with the users to inform them of the system's behavior. This will provide business users a natural language interface to interact with, monitor and control autonomous agents. In this work, we present a multi-agent orchestration framework to develop such proactive chatbots by discussing the types of skills that can be composed into agents and how to orchestrate these agents. Two use cases on a travel preapproval business process and a loan application business process are adopted to qualitatively analyze the proposed framework based on four criteria: performance, coding overhead, scalability, and agent overlap.

These three trends can help your enterprise transform data into information on demand that empowers every person, process, and system to be more agile and intelligent. As we bid farewell to 2019, many organizations now have hundreds of SaaS apps that increase the burden of data integration, especially when looking for a single view of your customer or identifying how well a product is delivered across your business. Although the application fabric has changed over the years, businesses are going through their own revolution as they struggle to manage the exponential increase in demand for data and insights from these apps across the enterprise. In 2020, companies that can prepare datasets quickly and accurately with the help of built-in intelligence and smart algorithms will come out on top. By enabling IT professionals to maintain the scale of data volumes and variety across both enterprise and cloud data sources, they can focus on supporting data democratization scenarios for immediate and repeatable self-service data needs.

Artificial Intelligence and its inherent bias may not be as judgmental as previously thought, at least in the case of home loans. It appears the use of algorithms for online mortgage lending can reduce discrimination against certain groups, including minorities, according to a recent study from the National Bureau of Economic Research. This could end up becoming the main tool in closing the racial wealth gap, especially as banks start using AI for lending decisions. The Breakdown You Need to Know: The study found that in person mortgage lenders typically reject minority applicants at a rate 6% higher than those with comparable economic backgrounds. However, when the application was online and involved an algorithm to make the decision, the acceptance and rejection rates were the same.