Day_7_Algorithmic_Bias_MA.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Day_7_Algorithmic_Bias_MA.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MAnn223/FutureMakers2022/blob/main/Day_7_Algorithmic_Bias_MA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "861ncVuLPeyF"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Day 7 Objectives:\n",
        "* To make you cognizant of algorithmic bias in some AI-based software, the reasons for its occurrence, and its effects on real-world inequalities and biases.\n",
        "* To help you think critically about the dangers that society might face, if automated systems have biases. \n"
      ],
      "metadata": {
        "id": "dd0uRp6DTnHg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Algorithmic Bias"
      ],
      "metadata": {
        "id": "Q48Qo4eZ4SC3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Play ''[Survival of the Best Fit](https://www.survivalofthebestfit.com/game/)'' to learn more about how AI might impact human resources and hiring processes in different fields. When you have completed this game, answer the following questions "
      ],
      "metadata": {
        "id": "CaX3TUFr65rL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Question 1\n",
        "How do you think Machine Learning or AI concepts were utilized in the design of this game?"
      ],
      "metadata": {
        "id": "SrMM7bWd73LA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Answer 1\n",
        "\n",
        "I think machine learning was used to create an algorithm to hire applicants similar to how I hired applicants at the initial part of the game. I think ML could have also been used for the parts of the game where I/the player interacts with the game such as when the manager/company asks you questions."
      ],
      "metadata": {
        "id": "MaaXX4lU_hVS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Question 2\n",
        "Did the ML model trained through your selection in the Survival of The Best Fit game seem to make biased decisions? \n",
        "\n",
        "If so, which elements/attributes of your selection likely caused the decision making process to become biased?"
      ],
      "metadata": {
        "id": "8wVYuhWf7qHt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Answer 2\n",
        "\n",
        "Yes the model seemed to make bias desicions. This probably because I had a bias when making selections so those biases were reflected on how the model selected applicants."
      ],
      "metadata": {
        "id": "Lsm0MCwy_kna"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Question 3\n",
        "Considering hiring, or selections for opportunities, more generally, what can cause a hiring algorithm such as the one shown in the game to make biased decisions? \n",
        "\n",
        "What can we do to make sure that any such automated algorithm is not affected by any data (or individual attributes) that should not be considered in a truly fair decision making process?"
      ],
      "metadata": {
        "id": "v6KmDo6A8sg3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Answer 3\n",
        "\n",
        "Personal bias and observer bias might have caused the hiring algorithm to make biased desicions. We need to have a diverse range of data that has equal representation and regulate the process though it will be difficult to maintain 0 bias."
      ],
      "metadata": {
        "id": "-EOYl7m-_ltg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Question 4\n",
        "How do you think we can reduce and remove algorithmic bias in algorithms for any task, but especially those that affect our health, well-being and access to resources?"
      ],
      "metadata": {
        "id": "CqtL0KkU9NpN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Answer 4\n",
        "\n",
        "I think again the data used to train the model needs to be a large amount of diverse and wide range data that shows equal representation and shows like mentioned in the slides, \"what should be\" so that everyone has equal access to resources and that mistakes aren't made when it comes to health. "
      ],
      "metadata": {
        "id": "djLJnS1z_nNl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Question 5\n",
        "\n",
        "Can you give another real-world example of a biased machine learning model, and share your ideas on how you make this model more fair, inclusive, and equitable? Please reflect on why you selected this specific biased model. \n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "WSilXw4y7EV6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Answer 5\n",
        "\n",
        "One real-world example is Amazon's biased hiring algorithm which is also similar to this game. The algorithm was biased against hriing women since the data used to train the model was based on the resumes sent in the last 10 years and most of those job applicants were men. "
      ],
      "metadata": {
        "id": "YjZbg8yn_jDT"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9cnZ9fCKDlGH"
      },
      "source": [
        "# **Upload this Day 7 Colab Notebook to your Github repository under \"Day 7\" folder. Also add your *Reflection* on today's learning in README.md**"
      ]
    }
  ]
}