Avoid These 6 Mistakes In Data Annotation

1
Avoid These 6 Mistakes In Data Annotation

• In traditional software development, the
  efficiency of the delivered product depends on
  its code quality. The same principle applies to
  Artificial Intelligence (AI) and Machine Learning
  (ML) projects. The quality of the data model
  output is dependent on the quality of its data
  labels.
• Poorly labeled data leads to poor quality of data
  models. Why does this matter so much?
  Low-quality AI and ML models can lead to
• An adverse impact on SEO and organic traffic (for
  product websites)
• An increase in customer churn
• Unethical errors or misrepresentations
• As data annotation (or labeling) is a continuous
  process, AI and ML models need continuous
  training to achieve accurate results. This
  requires data-driven organizations to avoid
  committing crucial mistakes in the annotation
  process.
• Here are six of the most common mistakes to avoid
  in data annotation
• projects

2

• Assuming The Labeling Schema Will Not Change
• A common mistake among data annotators is to
  design the labeling schema (in new projects) and
  assume that it will not change. As ML projects
  mature over time, data labeling schemas evolve
  and change over time. For example, labeling
  schemas can change in response to new products
  (or categories).
• Data annotation is expensive when performed
  before the labeling schema is mature and
  finalized. To avoid this mistake, data labelers
  must work closely with domain experts (working
  on the business problem to solve) and have
  multiple iterations to stabilize the schema.
  Programmatic labeling is another effective
  technique that can prevent unnecessary work and
  wastage.
• Insufficient Data Collection For The Project
• Data is essential to the success of any AI or ML
  project. For an accurate output, annotators must
  feed their projects with large volumes of
  high-quality data. Further, they must keep
  feeding quality data to ML models to understand
  and interpret the information. One common
  mistake in annotation projects is collecting
  insufficient data for the not-so-common
  variables.
• For instance, AI models are inadequately trained
  when annotators label their images for only
  commonly used variables. Deep learning data
  models need an ample quantity of high-quality
  data pieces. Hence, organizations must overcome
  the high cost of proper data collection, which
  can sometimes be impossible.

3

• Misinterpreting The Instructions
• Data annotators or labelers need clear
  instructions from their project managers on what
  they should annotate (or which objects to label).
  With misinterpreted instructions, annotators
  cannot create an accurate data model.
• Here is an example Labelers need to annotate a
  single object (using a bounded box). However,
  they may "misinterpret" the delivered
  instructions and end up "bounding" multiple
  objects in the image.
• To avoid this mistake, project managers must
  articulate clear and exhaustive instructions
  which annotators cannot misunderstand.
  Additionally, data annotators must double-check
  the provided instructions to understand their
  work clearly.
• Bias In Class Names
• This mistake is also related to the previous
  point of misinterpreting the instructions
  (especially when working with external
  annotators). Typically, external labelers are
  not involved in schema designing. Hence, they
  need proper instructions on how to label the
  data.
• Wrong instructions can lead to common mistakes
  such as
• Priming the user to pick one product category
  over another.
• Adding bias in annotation projects in the form of
  data labels or suggestions.
• Using "biased" class names like "Others," "Accesso
  ries," or "Miscellaneous."

4

• To avoid the common bias mistake, domain experts
  must have multiple interactions with the
  annotators, provide them with ample examples, and
  request their feedback.
• Selecting The Wrong Data Labeling Tools
• Due to the importance of data annotation, there
  is a growing global market for annotation tools,
  which is expected to grow at a healthy rate till
  2027. Organizations need to select the right
  tools to perform their data annotation. However,
  many organizations prefer to develop in-house
  labeling tools. Besides being expensive, in-house
  labeling tools are unable to keep pace with the
  growing complexity of annotation projects.
• Additionally, current annotation tools were
  developed in the earlier years of data analysis.
  They cannot handle Big Data volumes (and complex
  requirements) and lack the basic features of
  modern tools. To avoid this mistake, companies
  must look to invest in annotation tools developed
  by third-party data specialists.
• Missing Labels
• Data annotators often fail to label crucial
  objects in AI or ML projects. This can severely
  impact its quality. Human annotators can commit
  this mistake when they are not observant or
  simply miss some vital details. Missing labels
  are tedious and time-consuming to resolve for
  organizations, thus creating project delays and
  escalating project costs.
• To prevent this mistake, annotation projects must
  have a clear feedback system communicated to the
  annotators. Project managers must set up a
  proper review process, where annotation work is
  peer reviewed before the final approval.
  Additionally, organizations must hire experienced
  annotators with soft skills like an eye for
  detail and high patience levels.

5
Conclusion Accurate data labeling or annotation
is a vital cog in AI or ML projects and can
influence its output. The above-mentioned common
mistakes can undermine the data quality, making
it challenging to generate accurate results.
Data-dependent companies can avoid these common
mistakes by outsourcing their annotation work to
third-party professional companies. At EnFuse
Solutions, we offer specialized data annotation
services so that our customers can maximize
their investments in AI and ML technologies. We
customize our annotation services to each
client's specific needs. Let's collaborate for
your next AI or ML project. Connect with us
here. Read more Importance of Scale and Speed
in The Era of AI and ML