One Article Review
| Source |  Errata Security |
|---|---|
| Identifiant | 855273 |
| Date de publication | 2018-10-19 19:24:46 (vue: 2018-10-21 18:02:51) |
| Titre | Election interference from Uber and Lyft |
| Texte | Almost nothing can escape the taint of election interference. A good example is the announcements by Uber and Lyft that they'll provide free rides to the polls on election day. This well-meaning gesture nonetheless calls into question how this might influence the election."Free rides" to the polls is a common thing. Taxi companies have long offered such services for people in general. Political groups have long offered such services for their constituencies in particular. Political groups target retirement communities to get them to the polls, black churches have long had their "Souls to the Polls" program across the 37 states that allow early voting on Sundays.But with Uber and Lyft getting into this we now have concerns about "big data", "algorithms", and "hacking".As the various Facebook controversies have taught us, these companies have a lot of data on us that can reliably predict how we are going to vote. If their leaders wanted to, these companies could use this information in order to get those on one side of an issue to the polls. On hotly contested elections, it wouldn't take much to swing the result to one side.Even if they don't do this consciously, their various algorithms (often based on machine learning and AI) may do so accidentally. As is frequently demonstrated, unconscious biases can lead to real world consequences, like facial recognition systems being unable to read Asian faces.Lastly, it makes these companies prime targets for Russian hackers, who may take all these into account when trying to muck with elections. Or indeed, to simply claim that they did in order to call the results into question. Though to be fair, Russian hackers have so many other targets of opportunity. Messing with the traffic lights of a few cities would be enough to swing a presidential election, specifically targeting areas with certain voters with traffic jams making it difficult for them to get to the polls.Even if it's not "hackers" as such, many will want to game the system. For example, politically motivated drivers may choose to loiter in neighborhoods strongly on one side or the other, helping the right sorts of people vote at the expense of not helping the wrong people. Likewise, drivers might skew the numbers by deliberately hailing rides out of opposing neighborhoods and taking them them out of town, or to the right sorts of neighborhoods.I'm trying to figure out which Party this benefits the most. Let's take a look at rider demographics to start with, such as this post. It appears that income levels and gender are roughly evenly distributed.Ridership is skewed urban, with riders being 46% urban, 48% suburban, and 6% rural. In contrast, US population is 31% urban, 55% suburban, and 15% rural. Giving the increasing polarization among rural and urban voters, this strongly skews results in favor of Democrats.Likewise, the above numbers show that Uber ridership is strongly skewed to the younger generation, with 55% of the riders 34 and younger. This again strongly skews "free rides" by Uber and Lyft toward the Democrats. Though to be fair, the "over 65" crowd has long had an advantage as the parties have fallen over themselves to bus people from retirement communities to the polls (and that older people can get free time on weekdays to vote).Even if you are on the side that appears to benefit, this should still concern you. Our allegiance should first be to a robust and fa |
| Envoyé | Oui |
| Condensat | able about above accidentally account across advantage again algorithms all allegiance allow almost among announcements appears are areas asian based because being benefit benefits biases big black both burn bus but call calls can cars certain choose churches cities claim common communities companies concern concerns consciously consequences constituencies contested contrast controversies could crowd data day deliberately democrats demographics demonstrated dependence did difficult distributed don drivers early election elections enough entire escape even evenly example expense facebook faces facial fair fallen favor fewer figure first free frequently from future game gender general generation gesture get getting giving going good groups hackers hacking had hailing has have helping hotly how impact income increased increasing indeed individually influence information interference issue jams lastly lead leaders learning let levels lights like likewise loiter long look lot lyft machine makes making many may meaning mention messing might mind more most motivated much muck neighborhoods nonetheless not nothing now numbers offered often older one opportunity opposing order other out over owned particular parties party people polarization political politically politics polls population is post predict presidential prime probably program programs provide question read real recognition reliably result results retirement rider riders ridership rides right robust roughly rural russian second seems services should show side sides simply skew skewed skews small sorts souls specifically start states strongly suburban such sundays swing system systems taint take taking target targeting targets taught taxi them themselves these they thing those though time toward town traffic trending trying uber unable unconscious urban use various vote voters voting want wanted watching weekdays well when which who will wins world would wouldn wrong years younger |
| Tags | Guideline |
| Stories | Uber |
| Notes | |
| Move | 
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