MSGID: 1:317/3 646306f1   
   PID: hpt/lnx 1.9.0-cur 2019-01-08   
   TID: hpt/lnx 1.9.0-cur 2019-01-08   
    Tetris reveals how people respond to unfair AI    
      
     Date:   
         May 15, 2023   
     Source:   
         Cornell University   
     Summary:   
         An experiment in which two people play a modified version of   
         Tetris revealed that players who get fewer turns perceived the   
         other player as less likable, regardless of whether a person or   
         an algorithm allocated the turns.   
      
      
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   FULL STORY   
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   A Cornell University-led experiment in which two people play a modified   
   version of Tetris revealed that players who get fewer turns perceived   
   the other player as less likable, regardless of whether a person or an   
   algorithm allocated the turns.   
      
   Most studies on algorithmic fairness focus on the algorithm or the   
   decision itself, but researchers sought to explore the relationships   
   among the people affected by the decisions.   
      
   "We are starting to see a lot of situations in which AI makes decisions   
   on how resources should be distributed among people," said Malte Jung,   
   associate professor of information science, whose group conducted the   
   study. "We want to understand how that influences the way people perceive   
   one another and behave towards each other. We see more and more evidence   
   that machines mess with the way we interact with each other."  In an   
   earlier study, a robot chose which person to give a block to and studied   
   the reactions of each individual to the machine's allocation decisions.   
      
   "We noticed that every time the robot seemed to prefer one person,   
   the other one got upset," said Jung. "We wanted to study this further,   
   because we thought that, as machines making decisions becomes more a part   
   of the world -- whether it be a robot or an algorithm -- how does that   
   make a person feel?"  Using open-source software, Houston Claure -- the   
   study's first author and postdoctoral researcher at Yale University --   
   developed a two-player version of Tetris, in which players manipulate   
   falling geometric blocks in order to stack them without leaving gaps   
   before the blocks pile to the top of the screen.   
      
   Claure's version, Co-Tetris, allows two people (one at a time) to work   
   together to complete each round.   
      
   An "allocator" -- either human or AI, which was conveyed to the players -   
   - determines which player takes each turn. Jung and Claure devised their   
   experiment so that players would have either 90% of the turns (the "more"   
   condition), 10% ("less") or 50% ("equal").   
      
   The researchers found, predictably, that those who received fewer turns   
   were acutely aware that their partner got significantly more. But they   
   were surprised to find that feelings about it were largely the same   
   regardless of whether a human or an AI was doing the allocating.   
      
   The effect of these decisions is what the researchers have termed "machine   
   allocation behavior" -- similar to the established phenomenon of "resource   
   allocation behavior," the observable behavior people exhibit based on   
   allocation decisions. Jung said machine allocation behavior is "the   
   concept that there is this unique behavior that results from a machine   
   making a decision about how something gets allocated."  The researchers   
   also found that fairness didn't automatically lead to better game play   
   and performance. In fact, equal allocation of turns led, on average,   
   to a worse score than unequal allocation.   
      
   "If a strong player receives most of the blocks," Claure said, "the team   
   is going to do better. And if one person gets 90%, eventually they'll   
   get better at it than if two average players split the blocks."   
       * RELATED_TOPICS   
             o Mind_&_Brain   
                   # Consumer_Behavior # Behavior # Social_Psychology #   
                   Perception   
             o Computers_&_Math   
                   # Video_Games # Neural_Interfaces # Robotics #   
                   Computer_Programming   
       * RELATED_TERMS   
             o Massively_multiplayer_online_game o Milgram_experiment o   
             Alan_Turing o Random_variable o Early_childhood_education o   
             Familiarity_increases_liking o Tinnitus o Social_inclusion   
      
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   Story Source: Materials provided by Cornell_University. Original written   
   by Tom Fleischman, courtesy of the Cornell Chronicle. Note: Content may   
   be edited for style and length.   
      
      
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   Journal Reference:   
      1. Houston Claure, Seyun Kim, Rene' F. Kizilcec, Malte Jung. The social   
         consequences of Machine Allocation Behavior: Fairness, interpersonal   
         perceptions and performance. Computers in Human Behavior, 2023;   
         146: 107628 DOI: 10.1016/j.chb.2022.107628   
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   Link to news story:   
   https://www.sciencedaily.com/releases/2023/05/230515131943.htm   
      
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