>> senator bernie sanders on the trump administration. >> be have struggled since the inception of this country to fight against racism, sexism, xenophobia, and homophobia. we are telling mr. trump and his friends loudly and clearly that we are not going backwards. we are going forwards. >> senator marco rubio on a hearing on financial fraud targeting senior citizens. >> if you look at the list of from --care fraudulence fraudulence, they are coming from south florida and cuba. it is an outrage. it is grotesque. it has been extensively covered by the press in south florida. people may think seniors are

that -- >> all programming available on www.c-span.org by searching the video library. >> "washington journal" continues. host: welcome back. in this week spotlight on magazines, chelsea barabas joins us to talk about her article in "democracy journal" to talk aout her relationship with gate at transforming the workplace. people often think about automation. this is also driving power shifts. explain how. conversationof the around artificial intelligence in the workforce has been around the role it will play in automating all kinds of jobs in the workforce. in less talked about aspect of is the role that

artificial intelligence will aboutn decisions opportunities that are available to you in the workforce. a lot of my research has looked at the growing amount of data being used during hiring and recruitment for jobs as well as evaluating performance in the workplace. decisions about promotions and things like that. some people think this could be an opportunity for us to address a lot of the long-standing biases in the workforce around certain groups of people -- women, people of color -- being denied certain opportunities because of bias in hiring and promotion. there is also growing conversation around -- candidate really be used to make more

can data decisions -- really be used to make objective decisions and create a more fair labor market? host: you write that both skilled and unskilled workers are being increasingly evaluated by opaque algorithms. battles for the future of work will not play out between machine and man. be between the workers who generate data and those who analyze it and leverage it. how might this be used in a specific industry? guest: for example, in the high-tech industry, there has been a growing number of companies who have gone into the business of collecting information about the workforce and what individuals might have specific types of software development skills. is rightn area that for big data analysis of various

kinds. a lot of the work that software developers do create a digital foot trip -- footprint. if you are a coder, you are likely to take place in all my discussions -- online discussions. seen in the last few years, a growing number of companies in the business of entering that data from the internet and then compiling these large, either repositories that tried to encompass -- data repositories that tried to encompass as many workers in the high tech field as possible. companies that make dashboards and recommendation systems that identifys can use to skilled workers of a certain kind. people with certain kinds of

characteristics they are looking for. it can range from highly technical things to much more fuzzy concepts like your ability to work well in teams or levels of cooperativeness. ,hat we are seeing happen is because there is so much big data generated, it can fuel this type of recommendation system, and it is really changing the way recruiters go about their workflow and thinking and identifying -- especially workers at the top of the funnel. the systems are being used to employeespotential which is different from what a lot of us are familiar with. most of us think of the way we seek job opportunities as something where the company will

post the job description, and then you can apply. with these systems, recruiters have much more targeted attention on specific tenets -- candidates rather than relying on a public job posting. host: we are taking your calls about big data. if you live in the eastern or central time zone, call 202-748-8000. if you are in mountain or pacific time zones, call 202-748-8001. what types of workers are most at risk as employers leverage big data for their benefit? guest: if we are focusing on automation or these different types of evaluation tools -- automation is happening across the gambit. right now, what we are seeing are these kind of automated decision-making tools concentrated in a few industries. my work is particularly focused

on the high-tech center -- sector. the industry was going to the pains of feeling like the demand for skilled labor was outstripping the supply. in industries like that whether our chronic labor shortages, and we are seeing the pipeline for skilled workers expanding, these types of systems are being used. places, a other system that has been used for is low skilled work like call centers. most people who work in call centers have their calls recorded and constantly monitored. toy are held accountable hourly metrics such as how many they have -- how many people they have called. their promotion opportunities are based on those type of metrics.

a third interesting area we are starting to see this workasingly are areas of where we see digital platforms playing a larger and larger role in mediating interactions between customers and workers. an interesting one might be the transportation sector. with companies like uber and lyf t, taxi drivers are generating far more data about their professional behavior. the amount of work to do on a daily basis, their professionalism, this is valuable data that these driving platforms can use for a basis of promotion or incentivize asian -- incentivize asian -- incen tivization. also, the purchase of a new vehicle if they want to be able to finance the purchase of a large asset. hypothetically, this type of

professional data could be used to give insights into their cash flow, one activity, and their professional behavior. one thing that struck me in your piece was the quote that one team of data analyst found that a group of employees who listed the lord of the rings as their favorite books -- as one of their favorite books would be more likely to be at the head of tech companies. the type of person who fits this ideal profile would also likely heard the individuals in this current position. it would be about keeping those currently in power other than finding those looking for a job. is there a way to mitigate that? guest: i think so. data -- ahope of big

lot of the conversation was focused on this hope we could achieve more objective metrics for basing decisions. could we eliminate some of these sticky biases that have plagued our society since forever? the we have found is that data we generate reflects the world we live in. in this world, we have vastly different experiences based on our backgrounds and demographic features. if we do not take that into consideration, what run the risk of doing is replicating the existing biases we see in the world today. the tech center is a great example of this. the tech sector over the last few years has taken a lot of heat for not being very diverse. it is a largely white, largely male industry especially on the

engineering side of things. as a result, if you were to develop a recommendation system looking for key attributes and characteristics that might indicate who would be a good cto , those characteristics are likely to be more skewed to those people already in those positions. a majority of those people are white men. that means more white men are likely to come back. this is tricky, because the way we look at this is to sometimes think about eliminating race or gender from the equation. somehow take that out as a bias factor. the trick with that is that there are a lot of ways that race and gender and class can get double encoded into variables you would not think have anything to do with those things. for example, zip code is an indicator of race, because our

cities tend to be regulated -- dividedties tend to be up on racial lines. host: but take a call. we have john calling in from illinois. caller: good morning. i am working with journalism students at the college level. i am truce about what you found about linked in and social media students, graduates, mid-level people, what do you think of linked in and how that is being used? massivet has been a platform for recruiters and sourcing talent. again, i think it is interesting to look at the data available on

those websites and the decisions that are being made because of it. if you look at university college admissions, the way that applications are structured in and thetal dashboard -- digital dashboards that selection committees used to rank candidates are very important in shaping who gets in and who does not. similar to linkedin. it is the most dominant professional network available, and a lot of recruiters use it to find new talent. i have not done a lot of work specifically focusing on that. i think it is probably very influential and important these days. mentioned big data algorithms have the power to sort people into piles of worthy and not worthy. how regulated are the groups who collect and source the data?

guest: right now, not very regulated at all. the precedent for this kind of work comes from the fair credit reporting act in trying to regulate credit reporting agencies. but that is a very specific type of classification that really only is relevant or connected to credit reporting agencies as we have traditionally understood them. what we have seen with these quickly expanding group of third-party companies who do credit like reporting activities from a broader set of things, right now those are not really regulated under any specific law or regulation. but i think we can draw from the fair credit reporting act a useful precedent to think about

how we might regulating these things in the future, and how that might be important. credit reporting started in the mid 20th century, and some of the challenges for it was consumer information was being gathered and sold to companies, potential employers. the scores are being used as the basis to make decisions. but there was no obligation from the credit reporting agency or the person making the decision, a potential employer, to inform you that your credit score had been used to make that decision. if you were denied an opportunity based on your credit score, you had no way of knowing if that had been the case. if there was in accurate information in your credit score, you had no way to contest that or fix the record. the fair credit reporting act created due process and work to protect us as consumers and give us the opportunity to be

notified when decisions are being made on our behalf based on information collected. also to get access to that information, our credit scores, reviewed the information to have been compiled about us. three, it allows us to contest the accuracy of the information included in the report. to contest the types of decisions he made on your behalf using that information. i think we can borrow that framework for regulation, and think about notification, access to your information being used, and the process for contesting those decisions in testing for bias or discriminatory practices that could be built on top of them. host: john is on the line from massachusetts. good morning. caller: good morning. how are you doing? host: good. thank you. what is on your mind?

caller: i had a software company that did some early on large database things. i actually did some consulting for one of the big data recruiting database systems prior to linkedin. the whole notion that big data thatnd of doing anything is particularly good, and particularly the systems that parse largeo -- they have been sort of centralized into a couple of large companies. the whole idea of personal

computers and individual liberty and all these kind of things were thrown out the window. we designed a whole lot of makems to be able to information available to people on an individual level to get away from mainframes, controlling centralized, control of the machines. unfortunately, people give their data to these big systems, and they put themselves in a thing where people make sort of groupthink ideas about things. about softwarek specific talents

on linked in and hiring people based on job performance instead of people who open source and really no the engineering business is one thing. you have to understand -- i will give you an example. was boughtecently out and went public. it was bought out by microsoft were essentially the stock price 43%.nked in was up here was billions of dollars that was printed to give linked over to microsoft who controls a lot of the technology. this is kind of how it works. was, when the initial ipo for linkedin went out, it went out to very few

people. if you hire from link in -- linkedin, you are only going to get certain types of corporate people. think a certain way. typical -- a kind of really stops innovation. where thek at innovation comes from in the tech industry, a lot of it comes from overseas engineers and things like this, because they had to learn the systems from the ground up. in,t: care to weigh chelsea? i am trying to draw out a specific thing.

you said a lot of specific things in there. i am not really sure what to say to that, honestly. to our caller from lauren, ohio. caller: we definitely do not need marijuana smoking in the united states. that is for sure. first question on the magazine -- can they sell your information out? can they sell that information to other companies that deal with magazines? also, is he's been going to talk about the wiretapping on donald trump from president obama? tomorrow is going to be a good day. earlier point,

chelsea, can they sell your data to other people? guest: yes. there are large markets for the buying and selling of data. either from third parties who have collected it from services you used online. cases, those services have the right to your data and can do whatever they want, whether it be use it for internal purposes or sell it, particularly if the company goes bankrupt, often times if they are a digital company, they might sell that data in order to recover from the cost or debt. host: ok. let's move ahead to jack from davenport, iowa. caller: yes, you talked a little bit about stuff. in these videogames, two people collect data on what