A persona is an imaginary figure representing a segment of real people, and it is a communicative design technique aimed at enhanced user understanding. Through several decades of use, personas were data structures, static frameworks user attributes with no interactivity. A persona was a means to organize data about the imaginary person and to present information to the decision-makers. This wasn’t really actionable for most situations.
How personas and data work together
With increasing analytics data, personas can now be generated using big data and algorithmic approaches. This integration of personas and analytics offers impactful opportunities to shift personas from flat files of data presentation to interactive interfaces for analytics systems. These personas analytics systems provide both the empathic connection of personas and the rational insights of analytics. With persona analytics systems, the persona is no longer a static, flat file. Instead, they are operational modes of accessing user data. Combining personas and analytics also makes the user data less challenging to employ for those lacking the skills or desire to work with complex analytics. Another advantage of persona analytics systems is that one can create hundreds of data-driven personas to reflect the various behavioral and demographic nuances in the underlying user population.
A “personas as interfaces” approach offers the benefits of both personas and analytics systems and addresses each’s shortcomings. Transforming both the persona and analytics creation process, personas as interfaces provide both theoretical and practical implications for design, marketing, advertising, health care, and human resources, among other domains.
This persona as interface approach is the foundation of the persona analytics system, Automatic Persona Generation (APG). In pushing advancements of both persona and analytics conceptualization, development, and use, APG presents a multi-layered full-stack integration affording three levels of user data presentation, which are (a) the conceptual persona, (b) the analytical metrics, and (c) the foundational data.
APG generates casts of personas representing the user population, with each segment having a persona. Relying on regular data collection intervals, data-driven personas enrich the traditional persona with additional elements, such as user loyalty, sentiment analysis, and topics of interest, which are features requested by APG customers.
Leveraging intelligence system design concepts, APG identifies unique behavioral patterns of user interactions with products (i.e., these can be products, services, content, interface features, etc.) and then associates these unique patterns to demographic groups based on the strength of association to the unique pattern. After obtaining a grouped interaction matrix, we apply matrix factorization or other algorithms for identifying latent user interaction. Matrix factorization and related algorithms are particularly suited for reducing the dimensionality of large datasets by discerning latent factors.
How APG data-driven personas work
APG enriches the user segments produced by algorithms via adding an appropriate name, picture, social media comments, and related demographic attributes (e.g., marital status, educational level, occupation, etc.) via querying the audience profiles of prominent social media platforms. APG has an internal meta-tagged database of thousand of purchased copyright photos that are age, gender, and ethnically appropriate. The system also has an internal database of hundreds of thousands of names that are also age, gender, and ethnically appropriate. For example, for a persona of an Indian female in her twenties, APG automatically selects a popular name for females twenty years ago in India. The APG data-driven personas are then displayed to the users from the organization via the interactive online system.
APG employs the foundational user data that the system algorithms act upon, transforming this data into information about users. This algorithmic processing outcome is actionable metrics and measures about the user population (i.e., percentages, probabilities, weights, etc.) of the type that one would typically see in industry-standard analytics packages. Employing these actionable metrics is the next level of abstraction taken by APG. The result is a persona analytics system capable of presenting user insights at different granularity levels, with levels both integrated and appropriate to the task.
For example, C-level executives may want a high-level view of the users for which personas would be applicable. Operational managers may want a probabilistic view for which the analytics would appropriate. The implementers need to take direct user action, such as for a marketing campaign, for which the individual user data is more suitable.
Each level of the APG can be broken down as follows:
Conceptual level, personas. The highest level of abstraction, the conceptual level, is the set of personas that APG generates from the data using the method described above, with a default of ten personas. However, APG theoretically can generate as many personas as needed. The persona has nearly all the typical attributes that one finds in traditional flat-file persona profiles. However, in APG, personas as interfaces allow for dramatically increased interactivity in leveraging personas within organizations. Interactivity is provided such that the decision-maker can alter the default number to generate more or fewer personas, with the system currently set for between five and 15 personas. The system can allow for searching a set of personas or leveraging analytics to predict persona interests.
Analytics level: percentages, probabilities, and weights. At the analytics level, APG personas act as interfaces to the underlying information and data used to create the personas. The specific information may vary somewhat by the data source. Still, the analytics level will reflect the metrics and measures generated from the foundational user data and create the personas. In APG, the personas provide affordance to the various analytics information via clickable icons on the persona interface. For example, APG displays the percentage of the entire user population that a particular persona is representing. This analytic insight is valuable for decision-makers to determine the importance of designing or developing for a specific persona and helps address the issue of the persona’s validity in representing actual users.
User level: individual data. Leveraging the demographic metadata from the underlying factorization algorithm, decision-makers can access the specific user level (i.e., individual or aggregate) directly within APG. The numerical user data (in various forms) are the foundation of the personas and analytics.
The implications of data-driven personas
The conceptual shift of personas from flat files to personas as interfaces for enhanced user understanding opens new possibilities for interaction among decision-makers, personas, and analytics. Using data-driven personas embedded as the interfaces to analytics systems, decision-makers can, for example, imbue analysis systems with the benefit of personas to form a psychological bond, via empathy, between stakeholders and user data and still have access to the practical user numbers. There are several practical implications for managers and practitioners. Namely, personas are now actionable, as the personas accurately reflect the underlying user data. This full-stack implementation aspect has not been available with either personas or analytics previously.
APG is a fully functional system deployed with real client organizations. Please visit https://persona.qcri.org to see a demo.
This content was written by Qatar Computing Research Institute, Hamad Bin Khalifa University, a member of Qatar Foundation. It was not written by MIT Technology Review’s editorial staff.
The Download: Algorithms’ shame trap, and London’s safer road crossings
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
How algorithms trap us in a cycle of shame
Working in finance at the beginning of the 2008 financial crisis, mathematician Cathy O’Neil got a firsthand look at how much people trusted algorithms—and how much destruction they were causing. Disheartened, she moved to the tech industry, but encountered the same blind faith. After leaving, she wrote a book in 2016 that dismantled the idea that algorithms are objective.
O’Neil showed how every algorithm is trained on historical data to recognize patterns, and how they break down in damaging ways. Algorithms designed to predict the chance of re-arrest, for example, can unfairly burden people, typically people of color, who are poor, live in the wrong neighborhood, or have untreated mental-health problems or addictions.
Over time, she came to realize another significant factor that was reinforcing these inequities: shame. Society has been shaming people for things they have no choice or voice in, such as weight or addiction problems, and weaponizing that humiliation. The next step, O’Neill recognized, was fighting back. Read the full story.
London is experimenting with traffic lights that put pedestrians first
The news: For pedestrians, walking in a city can be like navigating an obstacle course. Transport for London, the public body behind transport services in the British capital, has been testing a new type of crossing designed to make getting around the busy streets safer and easier.
How does it work? Instead of waiting for the “green man” as a signal to cross the road, pedestrians will encounter green as the default setting when they approach one of 18 crossings around the city. The light changes to red only when the sensor detects an approaching vehicle—a first in the UK.
How’s it been received? After a trial of nine months, the data is encouraging: there is virtually no impact on traffic, it saves pedestrians time, and it makes them 13% more likely to comply with traffic signals. Read the full story.
Check out these stories from our new Urbanism issue. You can read the full magazine for yourself and subscribe to get future editions delivered to your door for just $120 a year.
– How social media filters are helping people to explore their gender identity.
– The limitations of tree-planting as a way to mitigate climate change.
Podcast: Who watches the AI that watches students?
A boy wrote about his suicide attempt. He didn’t realize his school’s software was watching. While schools commonly use AI to sift through students’ digital lives and flag keywords that may be considered concerning, critics ask: at what cost to privacy? We delve into this story, and the wider world of school surveillance, in the latest episode of our award-winning podcast, In Machines We Trust.
Check it out here.
ICYMI: Our TR35 list of innovators for 2022
In case you missed it yesterday, our annual TR35 list of the most exciting young minds aged 35 and under is now out! Read it online here or subscribe to read about them in the print edition of our new Urbanism issue here.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 There’s now a crazy patchwork of abortion laws in the US
Overturning Roe has triggered a legal quagmire—including some abortion laws that contract others within the same state. (FT $)
+ Protestors are doxxing the Supreme Court on TikTok. (Motherboard)
+ Planned Parenthood’s abortion scheduling tool could share data. (WP $)
+ Here’s the kind of data state authorities could try to use to prosecute. (WSJ $)
+ Tech firms need to be transparent about what they’re asked to share. (WP $)
+ Here’s what people in the trigger states are Googling. (Vox)
2 Chinese students were lured into spying for Beijing
The recent graduates were tasked with translating hacked documents. (FT $)
+ The FBI accused him of spying for China. It ruined his life. (MIT Technology Review)
3 Why it’s time to adjust our expectations of AI
Researchers are getting fed up with the hype. (WSJ $)
+ Meta still wants to build intelligent machines that learn like humans, though. (Spectrum IEEE)
+ Yann LeCun has a bold new vision for the future of AI. (MIT Technology Review)
+ Understanding how the brain’s neurons really work will aid better AI models. (Economist $)
4 Bitcoin is facing its biggest drop in more than 10 years
The age of freewheeling growth really is coming to an end. (Bloomberg $)
+ The crash is a threat to funds worth millions stolen by North Korea. (Reuters)
+ The cryptoapocalypse could worsen before it levels out. (The Guardian)
+ The EU is one step closer towards regulating crypto. (Reuters)
5 Singapore’s new online safety laws are a thinly-veiled power grab
Empowering its authoritarian government to exert even greater control over civilians. (Rest of World)
6 Recommendations algorithms require effort to work properly
Telling them what you like makes it more likely it’ll present you with decent suggestions. (The Verge)
8 Inside YouTube’s meta world of video critique
Video creators analyzing other video creators makes for compelling watching. (NYT $)
+ Long-form videos are helping creators to stave off creative burnout. (NBC)
9 Time-pressed daters are vetting potential suitors over video chat
To get the lay of the land before committing to an IRL meet-up. (The Atlantic $)
10 How fandoms shaped the internet
For better—and for worse. (New Yorker $)
Quote of the day
“This is no mere monkey business.”
—A lawsuit filed by Yuga Labs, the creators of the Bored Ape NFT collection, against conceptual artists Ryder Ripps, claims Ripps copied their distinctive simian artwork, Gizmodo reports.
The big story
This restaurant duo want a zero-carbon food system. Can it happen?
When Karen Leibowitz and Anthony Myint opened The Perennial, the most ambitious and expensive restaurant of their careers, they had a grand vision: they wanted it to be completely carbon-neutral. Their “laboratory of environmentalism in the food world” opened in San Francisco in January 2016, and its pièce de résistance was serving meat with a dramatically lower carbon footprint than normal.
Myint and Leibowitz realized they were on to something much bigger—and that the easiest, most practical way to tackle global warming might be through food. But they also realized that what has been called the “country’s most sustainable restaurant” couldn’t fix the broken system by itself. So in early 2019, they dared themselves to do something else that nobody expected. They shut The Perennial down. Read the full story.
We can still have nice things
+ A look inside the UK’s blossoming trainspotting scene (don’t worry, it’s nothing to do with the Irvine Welsh novel of the same name.)
+ This is the very definition of a burn.
+ A solid science joke.
+ This amusing Twitter account compiles some of the strangest public Spotify playlists out there (Shout out to Rappers With Memory Problems)
+ Have you been lucky enough to see any of these weird and wonderful buildings in person?
The US Supreme Court just gutted the EPA’s power to regulate emissions
What was the ruling?
The decision states that the EPA’s actions in a 2015 rule, which included caps on emissions from power plants, overstepped the agency’s authority.
“Capping carbon dioxide emissions at a level that will force a nationwide transition away from the use of coal to generate electricity may be a sensible ‘solution to the crisis of the day,’” the decision reads. “But it is not plausible that Congress gave EPA the authority to adopt on its own such a regulatory scheme.”
Only Congress has the power to make “a decision of such magnitude and consequence,” it continues.
This decision is likely to have “broad implications,” says Deborah Sivas, an environmental law professor at Stanford University. The court is not only constraining what the EPA can do on climate policy going forward, she adds; this opinion “seems to be a major blow for agency deference,” meaning that other agencies could face limitations in the future as well.
The ruling, which is the latest in a string of bombshell cases from the court, fell largely along ideological lines. Chief Justice John Roberts authored the majority opinion, and he was joined by his fellow conservatives: Justices Samuel Alito, Amy Coney Barrett, Neil Gorsuch, Brett Kavanaugh, and Clarence Thomas. Justices Stephen Breyer, Elena Kagan, and Sonia Sotomayor dissented.
What is the decision all about?
The main question in the case was how much power the EPA should have to regulate carbon emissions and what it should be allowed to do to accomplish that job. That question was occcasioned by a 2015 EPA rule called the Clean Power Plan.
The Clean Power Plan targeted greenhouse-gas emissions from power plants, requiring each state to make a plan to cut emissions and submit it to the federal government.
Several states and private groups immediately challenged the Clean Power Plan when it was released, calling it an overreach on the part of the agency, and the Supreme Court put it on hold in 2016. After a repeal of the plan during Donald Trump’s presidency and some legal back-and-forth, a Washington, DC, district court ruled in January 2021 that the Clean Power Plan did fall within the EPA’s authority.
How to track your period safely post-Roe
3. After you delete your app, ask the app provider to delete your data. Just because you removed the app from your phone does not mean the company has gotten rid of your records. In fact, California is the only state where they are legally required to delete your data. Still, many companies are willing to delete it upon request. Here’s a helpful guide from the Washington Post that walks you through how you can do this.
Here’s how to safely track your period without an app.
1. Use a spreadsheet. It’s relatively easy to re-create the functions of a period tracker in a spreadsheet by listing out the dates of your past periods and figuring out the average length of time from the first day of one to the first day of the next. You can turn to one of the many templates already available online, like the period tracker created by Aufrichtig and the Menstrual Cycle Calendar and Period Tracker created by Laura Cutler. If you enjoy the science-y aspect of period apps, templates offer the ability to send yourself reminders about upcoming periods, record symptoms, and track blood flow.
2. Use a digital calendar. If spreadsheets make you dizzy and your entire life is on a digital calendar already, try making your period a recurring event, suggests Emory University student Alexa Mohsenzadeh, who made a TikTok video demonstrating the process.
Mohsenzadeh says that she doesn’t miss apps. “I can tailor this to my needs and add notes about how I’m feeling and see if it’s correlated to my period,” she says. “You just have to input it once.”
3. Go analog and use a notebook or paper planner. We’re a technology publication, but the fact is that the safest way to keep your menstrual data from being accessible to others is to take it offline. You can invest in a paper planner or just use a notebook to keep track of your period and how you’re feeling.
If that sounds like too much work, and you’re looking for a simple, no-nonsense template, try the free, printable Menstrual Cycle Diary available from the University of British Columbia’s Centre for Menstrual Cycle and Ovulation Research.
4. If your state is unlikely to ban abortion, you might still be able to safely use a period-tracking app. The crucial thing will be to choose one that has clear privacy settings and has publicly promised not to share user data with authorities. Quintin says Clue is a good option because it’s beholden to EU privacy laws and has gone on the record with its promise not to share information with authorities.