Monthly Archives: April 2019

COUNTERACTING IDENTITY FRAUD IN THE INFORMATION AGE

With the Digital Age comes an increase in the amount of personal data that is stored electronically. An article by Kurt Saunders and Bruce Zucker highlights the dangers of identity theft in a time of digital repositories of personal information. Banks, schools, hospitals, and countless other businesses now store information like our social security numbers, dates of birth, and addresses in online databases that can be hacked. Grinnell College students experienced a social security number hack first hand this year! Digitized information makes it easier for thieves to assume someone else’s identity and withdraw money from their bank account or take out credit cards in their name without the intention of ever paying the money back. As such, the question is, just how can such a matter affect a person, and how is it being combatted.

There are two practical issues related to identity theft. A person’s identity is a fundamental component of financial institutions; banks don’t give loans to those unlikely to repay them. A person can open lines of credit with no intent to repay under a false identity, thus gaining access to otherwise unattainable resources and damaging the victim’s ability to do the same. The real-world case of Terry Rogan, a man from Michigan, depicts issues related to law enforcement; Rogan’s identity was assumed by a fleeing convict, who later had an arrest warrant for murder issued in Los Angeles. The real Rogan’s information was placed in a national database, and every subsequent interaction he had with law-enforcement back in Michigan was governed by that. Rogan was detained several times on suspicion of a murder he didn’t commit after otherwise routine traffic stops.  This scenario is not too far fetched for anyone, and brings the question, on just what is being done to counteract potential threats.

Due to identity theft becoming a real threat to almost anyone in the Digital Age, repercussions have been made in order to keep people and their information safe. In what was necessary, the government initially did not have laws that would qualify online identity theft as a criminal offense. Originally, they could only utilize laws that made unpermitted payments and fraud illegal, but this failed to reach the extent of being able to proactively combat against the growing threat of identity theft. Eventually in 1998, the Identity Theft and Assumption Deterrence Act was passed. This act allowed for identity theft to be combatted against. First, it allowed for the government to charge against online identity theft as a criminal offense with severe prosecution against perpetrators, and the act also allowed more people to become more aware of online safety due to federal regulations to educate people in schools of the dangers of the web. While it’s hard to say if such actions will truly prevent people from suffering identity theft, it’s a start towards combatting against the crime, as people recognize it’s severity to the victims and the public, as well as prompting them to take a stand against such crimes in order to combat against it.

Determining Authenticity of Video Evidence in the Age of Artificial Intelligence and in the wake of Deepfake videos

Deepfake videos utilize artificial intelligence and machine learning algorithms to superimpose the faces of famous people, or even ordinary folks, onto the bodies of other people in different videos. Deepfake videos appear very realistic because machine learning allows the algorithm to continuously improve the image. This technology is often used in a sinister way to incriminate people in pornographic videos or to generate visual ‘evidence’ of events that never actually happened. The open-source artificial intelligence tool, TenserFlow, has been misused as a tool for creating Deepfake videos as the program relies on machine learning and image processing.

“Deepfake technology does its own google image search and scours through social media and can, by itself, replace faces in videos.  The program improves itself independently through machine learning.  Using this tech, anyone can create fake videos including pornographic videos of just about anyone.  Also, Deepfakes can be spread rapidly considering how quickly media is consumed and reproduced online. There is another technology that lies in this same vein, such as tech that can automatically alter images, and tech that can recreate voices.  The implications of this are that we may be looking at a future where people can create photos, videos, and audio of someone doing things they never did and saying things they never said.”

Development of crafty fake videos techniques influences jurisdictive decisions in courts as well. Videos and images have been strong evidence to prove eyewitnesses, but now it came to the situation that the validity of videos and images may be skeptical. Rather, videos might need to be proved by eyewitnesses. Another problem is that, yet, there are not many experts who know well about artificial intelligence and are capable of distinguishing fake videos from the real videos.

While it is only a matter of time before fake videos keep improving their performance, research in this field is sparse and so too are the necessary tools that could help stop the perpetuation of deep fake videos. Researchers have begun to make algorithms that can make these deep fake videos more easily identified but this same technology can also further advance the creation of these videos.

 

Sean Lee: Third paragraph, editing, and posting

Zaria: First paragraph

Gray: Second paragraph

TJ: Fourth paragraph

Flatow: Improving Healthcare, One Search At A Time

This NPR piece was done on “Science Friday” and featured a conversation between Ira Flatow, and guest Dr. Eric Horvitz, a Microsoft Research scientist. The discussion follows the role of increasing internet searches for health-related topics in discovering critical information about the interactions between drugs. Horvitz and his team were able to find a connection between Pravastatin, a drug used to lower cholesterol, and Paroxetine, an antidepressant. When combined, these drugs can cause hyperglycemia. It is noted that these two drugs are both quite common and yet no drug testing had proven the adverse side effects of combining the two. While Horvitz clarifies that in this case, another study at Stanford had found the connection between the two drugs, Horvitz’s team aimed to see if they could have predicted what the study found through looking at search history. As a result, Horvitz went back to a year before the Stanford study, 2010, and, with consent, conducted an analysis that showed similar findings. Thus, this technology could be used in the future too, without scientific study, predict negative interactions between medicine.

While this specific incidence does not necessarily pose a significant ethical question with regards to privacy, as consent was asked, how technology is used in this application does bring about some issues. For example, while we hold Microsoft to a high standard because we know the company, what about other companies using similar technologies, both for good and bad? Will privacy always be prioritized?

The article also highlights the current issue in the U.S., and around the world, of “cyberchondria,” which is a phenomenon where people tend to look to the internet to answer what their symptoms mean, only to escalate their symptoms to the worst extreme. Perhaps with the transition to more forms of e-health, this cyberchondria issue will become less of a problem.

ProPublica the Criminal Justice System

Northpointe’s algorithm takes real people, with complex identities and lives, and reduces them to abstract equations. The job of a judge is to listen to the evidence presented by the defence and the prosecution, consider the whole picture, and issue a decision based on the available facts and the the law. When something like a mandatory minimum is introduced into the legal system, it takes part of the process of thinking and adjudication away from the judicial process. It is certainly true that the biases and prejudices of our greater american public are already reflected in our judges. When Northpointe’s algorithm, however, is used in matters of jurisprudence to influence bond and sentencing two things are occurring. First, we are relying on computers and programs to do our thinking for us and pretending that a number can accurately represent the complexities of a human life. Second, we are obscuring the biased process which gives a false notion of objectivity.

 

I wonder how ethicists, sociologists, lawmakers, and community members were consulted to determine what questions should be asked and how they should be weighted? Even seemingly innocuous questions such as where someone lives are tied up in issues of racial and economic segregation in America.

 

Thoughts on Monday’s Discussion

Following our discussion on Monday, I have been thinking a lot about one of the comment that Professor Rodrigues mentioned at one point – the best time to be scheduled for sentencing is early in the morning or after lunch. While I had seen lawyers on TV and such trying to get these times, I still was still associating this more with the realm of fiction. It is incredibly alarming that whether or not Judge X has had their pastrami sandwich from Frank’s determines whether or not and to what degree somebody will get probation, fined, or imprisoned. A lunch, or lack thereof, should not be what alters the course of somebody’s life. With regards to decision-making, this is one way where I see how technology such as this could potentially make a difference. To my knowledge, an algorithm can’t get hangry or cranky. However, it is also clear that a biased algorithm with a less than stellar accuracy level cannot be the better option. In my discussion group, we briefly touched on an ethical dilemma that could result from this. What if the algorithm got to a point where it was better and less biased than judges at predicting recidivism rates, yet still not completely accurate? Would it be better to use it or not?

As TJ said in class, it is also crucial that people who are responsible for creating this technology take responsibility and work to make it less biased. I don’t know if this will be possible soon, though; the judicial system, being already extremely racially biased, influences whether or not people can get jobs, which affects the algorithm by tying having a job or not to the risk score created. Thus, the fastest way may not be turning to technology, but instead paying more attention to the people making the decisions.

Facial recognition follow up reading

Some other resources for learning about the social impacts and ethical questions of facial recognition:

TheVerge.com covers facial recognition quite a bit. Here’s some recent stories.

One of those stories talks about a tool for airports called Biometric Exit that 15 airports have already put into use. The Department of Homeland Security wants it to be in most US airports within the next four years.

If you don’t like that, maybe you can change your face. In just one more example of resilient creativity in the digital age, folks have been working on how to defeat FR with (pretty rad) hairstyles and makeup.

An overview of the ethics of FR by the Center for Digital Ethics and Policy.

That post mentions the Federal Trade Commission’s (somewhat outdated) best practices for FR, which are suggestions, not regulations.

Is there such a thing as ethical facial recognition? Kairos is one company trying to prove that there is. See their About page and a recent blog post on what would constitute ethical facial recognition, keeping in mind that this is marketing material for a specific company.

There are surely many others–if you have a suggested reading, let me know and I’ll add it.

Who is responsible?

Our ethical discussion on Monday spanned many ethical issues, with consequences varying from companies being able to better target advertisements to Facebook users to technology potentially redefining the way we conceptualize war. I am consistently realizing that to refer to “ethical issues of technology” could mean almost anything.

Many of the morally dubious developments we have discussed remind me in some way of Caleb Thompson’s reflection. When Amazon markets facial recognition software to state law enforcement without testing it for racial bias, developers were on the other end coding small parts that built up to the final product. Individuals develop algorithms to collect your information from the internet, and others design advertisements specifically targeted at your psychological profile. Engineers build robots with the capacity to kill people autonomously. In these situations, who bears the responsibility to ensure that technology is developed in an ethical manner? Should individual developers, coders, and engineers be responsible or everything they create, or do those with broader views of the projects bear responsibility? Clearly, government regulation would help define some of these questions. However, the government often doesn’t fully conceptualize how technology is developing. Or sometimes, as in cases of weapon development, they are the ones creating the dangerous technology. Finally, what is the role of the formal computing profession? We read the ACM code of ethics, which provided some helpful guidelines for its members. Should groups like these be doing more to establish the norm that individual computing professionals are in some part responsible for what they create?

Angwin: Machine Bias, Singer: Amazon’s Facial Recognition

The New York Times article covered the flawed facial recognition technology created by Amazon. With a database of 25,00 publicly available mugshots, the “technology incorrectly matched lawmakers with people who had been charged with a crime.” Although the software initially served to prevent human trafficking, facial recognition is fast becoming a top target for civil liberties groups and privacy experts. This way, civil liberties groups view it as a surveillance system to lower political protests by eliminating anonymity. In the wrong hands, facial recognition can be inseparable from a tool of social control.

Northpointe’s algorithm has been shown to turn up flawed results. Broward County, Florida uses the score in pretrial hearings, and ProPublica’s research proved it remarkably unreliable. Only 20 percent of those predicted to commit violent crimes did so, and when looking at all crimes it was only slightly more reliable “than a coin flip.” Moreover, it turned up black defendants as more likely to be future criminals two times as much as whites, and also incorrectly labeled whites as low risk more frequently.

Regarding ethics, we determined multiple options for possible outcomes. One extreme would be for technologies such as the risk assessment algorithm and Amazon’s facial recognition to continue to be used in their current capacities. This would mean bias being perpetuated in yet another mode. The opposing option for the former would be for these technologies to be banned entirely. While this would prevent the fundamental flaws currently happening with both the algorithm and facial recognition, there are also benefits that can be had from using this technology, were bias, specifically against people of color, to be removed. As we spoke about in class, just because a product has a high success rate, that does not mean that the success rate of predicting is equal among everybody. Thus, while an average looks successful, the accuracy can be completely skewed. Therefore, the third alternative, which is the alternative our group said we could live with, is to ban products like these until they can be re-thought and created to display no bias.

Fake News and Lethal Robots

In the article, “Fake News and Partisan Epistemology”, Regina Rini expresses concerns about the epidemic of fake news being spread so widely and rapidly on social media platforms today. Fake news is deliberately deceptive, it is meant to catch the eye and catch clicks for the purpose of generating revenue for someone’s website. A variety of epistemic virtues are strangely abandoned on the platform of social media. The article investigates what features of social media make it so easy for people to abandon epistemic virtue. Rini points out that partisanship is the reason that people are more likely to surrender epistemic virtue and readily jump upon wild and outrageous conspiracy theories which to anyone with a critical eye would warrant some skepticism. The way partisanship manifests itself as an opponent to truth is when people share particular political affiliations with others. Those people are seen as closer to themselves and are seen as right simply because the receiver of the fake news assumes that anyone who shares their political opinions must be feeding them proper information. This mainly has to do as well with our willingness to believe testimony outright rather than cross checking every bit of information that is fed to us. Believing testimony is individually reasonable and Rini argues that the mechanism of social media takes advantage of this individually reasonable behavior and co-opts the space of testimony in order to spread misinformation. Rini believes a possible solution to this phenomenon is for social media platforms to flag individual accounts which regularly spread misinformation and to create a sort of score which measures credibility thereby making people take more responsibility for the things they post.

This article begins to consider the implications of machines used as military weaponry. Specifically, should machines be able to kill people in combat? It’s clear that even things that are not designed as weapons have the power and potential to be used as weapons..even a toaster. While robots themselves were never intended to replace humans in war they serve as a way to potentially decrease casualties while also being able to make the choice to kill another person at will. The article explains that this is exactly the issue in that robots have no “will” or morality and than even while war literally means death the only people who can be the perpetrators of death must also be willing to be the recipient of it themselves.
After detailing the various background information regarding the types of weapons and the laws of war, the authors proceed to address the major question of their essay: “should we relinquish the decision to kill a human to a non-human machine?” (134). In order to treat this profound question, the authors expound on the philosophical definition of a human being, a being with intrinsic dignity and rights according to Immanuel Kant. Using a robot to kill a human treats a human being as a mere object, and therefore denies human dignity. Furthermore, the authors discuss morality as an essentially human characteristic, and maintain that a robot could only imitate moral actions, without being in itself moral. They also discuss LAWS as being potentially dishonorable, in that they negate the risk of immediate sacrifice inherent in war. Without the potential for sacrifice, the use of robots becomes cowardly, and thus contrary to what is considered honorable military conduct. The authors conclude by postulating a complete ban on autonomous weapons systems, much like the current status of chemical and gas weapons, considered too heinous to be tolerated.

Luerweg: The Internet knows You Better Than Your Spouse Does

“The Internet knows You Better Than Your Spouse Does,” by Frank Luerweg describes how internet algorithms use psychology to identify personality traits of users. One algorithm used a small number of Facebook likes to pinpoint the “Big Five dimensions of personality.” With only ten likes, it could describe someone as accurately as a co-worker of theirs. This type of technology extends beyond internet algorithms. Studies observing participants’ eye movements were able to accurately describe their personalities based on where they look when walking around a college campus and shopping. Cameras on our computers and smartphones have the potential to read our emotions.

Even though the algorithms are based on personal information, facial expressions and psychological traces are maliciously and commercially used, there are some cases for which algorithms were used to better diagnose and treat psychological disorders and prevent suicide. In some research, the language that people typed and spoke on the phone were gathered and analyzed through the algorithm and it could determine the precursors of suicide and severe depression quite accurately. Moreover, a research team gathered every data from a tester from GPS data to phone calls and what he read on the phone. The team precisely analyzed and could better treat the patient with the result in which level the patient is suffering with a bipolar disorder.

In spite of potential positive effects from such advancements in technology, there are still potentially great drawbacks towards the Internet’s ability to recognize a user. While recognizing a user is mostly used towards commercial matters such as advertisements to suit the user’s preference, such information can result in modern day machine’s being able to use the algorithm in order to correlate further into a person. Such can include personal information such as using photos as facial recognition, with common photo algorithms pointing towards people in manners such as personal information like their orientation, or in certain cases, to recognize if a person has potential criminal tendencies. All this however, comes down to correlating through the given information, but as technology advances, they can lead towards further accuracy, and can reveal more of a person than what was intended by that user. In the end, even the slightest comment or photo on the Internet could open a book into a person’s world.

Paragraphs, in oder, by Georgia, Sean L., and Gabriel. Compiled by Georgia.